Skip to main content

Conclusion to Part II

  • Chapter
  • First Online:
Probability and Social Science

Part of the book series: Methodos Series ((METH,volume 10))

  • 1244 Accesses

Abstract

After this detailed examination of the links between population sciences, statistics, and probability, we can now provide clearer answers to some of the questions underlying Part II of our book. First, what is the intensity of the ties between population sciences and probability, partly mediated by statistics? Second, what is the nature of the connections between probability, social sciences, and causal inference? Third, does cumulativity exist in these sciences, and, if so, what form does it take?

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aalen, O. O. (1975). Statistical inference for a family of counting processes. PhD thesis, University of California, Berkeley.

    Google Scholar 

  • Aalen, O. O. (1978). Nonparametric inference for a family of counting processes. The Annals of Statistics, 6(4), 701–726.

    Google Scholar 

  • Aalen, O. O., & Tretli, S. (1999). Analysing incidence of testis cancer by means of a frailty model. Cancer Causes & Control, 10, 285–292.

    Google Scholar 

  • Aalen, O. O., Borgan, Ø., Keiding, N., & Thorman, J. (1980). Interaction between life history events. Nonparametric analysis for prospective and retrospective data in the presence of censoring. Scandinavian Journal of Statistics, 7, 161–171.

    Google Scholar 

  • Aalen, O. O., Borgan, Ø., & Gjessing, H. K. (2008). Survival and event history analysis. New York: Springer.

    Google Scholar 

  • Aalen, O. O., Andersen, P. K., Borgan, O., Gill, R. D., & Keiding, N. (2009). History of applications of martingales in survival analysis. Journ@l Electronique d’Histoire des Probabilités et de la Statistique, 5(1), 1–28.

    Google Scholar 

  • Abel, N. H. (1826). Untersuchung der Functionen zweier unabhängig veränderlichen Gröszen x und y, wie f(x, y), welche die Eigenschaft haben, dasz f[z, f(x,y)] eine symmetrische Function von z, x und y ist. Journal Reine und angewandte Mathematik (Crelle’s Journal), 1, 11–15.

    Google Scholar 

  • Aczèl, J., Forte, B., & Ng, C. T. (1974). Why the Shannon and Hartley entropies are ‘natural’. Advanced Applied Probabilities, 6, 131–146.

    Google Scholar 

  • Agazzi, E. (1985). Commensurability, incommensurability and cumulativity in scientific knowledge. Erkenntnis, 22(1–3), 51–77.

    Google Scholar 

  • Agliardi, E. (2004). Axiomatization and economic theories: Some remarks. Revue Economique, 55(1), 123–129.

    Google Scholar 

  • Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica, 21(4), 503–546.

    Google Scholar 

  • Andersen, P. K., & Gill, R. D. (1982). Cox’s regression model for counting processes: A large sample study. The Annals of Statistics, 10, 1100–1120.

    Google Scholar 

  • Andersen, P. K., Borgan, Ø., Gill, R. D., & Keiding, N. (1993). Statistical models based on counting processes. New York/Berlin/Heidelberg: Springer.

    Google Scholar 

  • Arbuthnott, J. (1710). An argument for divine providence, taken from the constant regularity observ’d in the birth of both sexes. Philosophical Transactions of the Royal Society of London, 27, 186–190.

    Google Scholar 

  • Aristotle. (around 330 B.C.). Nichomachean ethics. The Internet Classic Archive (W. D. Ross, Trans.). http://classics.mit.edu/Aristotle/nicomachaen.html. Accessed August 30, 2011.

  • Aristotle. (around 330 B.C.). Physics. The Internet Classic Archive (R. P. Hardie & R. K. Gaye, Trans.). http://classics.mit.edu/Aristotle/physics.html. Accessed August 30, 2011.

  • Aristotle. (around 330 B.C.). Politics. The Internet Classic Archive (B. Jowett, Trans.). http://classics.mit.edu/Aristotle/politics.html. Accessed August 30, 2011.

  • Aristotle. (around 330 B.C.). Rhetoric. The Internet Classic Archive: translated by Roberts, W. R. http://classics.mit.edu/Aristotle/rethoric.html. Accessed August 30, 2011.

  • Aristotle. (around 330 B.C.). Topics. The Internet Classic Archive (W. A. Pickard-Cambridge, Trans.). http://classics.mit.edu/Aristotle/topics.html. Accessed August 30, 2011.

  • Armatte, M. (2004). L’axiomatisation et les théories économiques: un commentaire. Revue Economique, 55(1), 130–142.

    Google Scholar 

  • Armatte, M. (2005). Lucien March (1859–1933). Une statistique mathématique sans probabilité? Journ@l Electronique d’Histoire des Probabilités et de la Statistique, 1(1), 1–19.

    Google Scholar 

  • Arnauld, A., & Nicole, P. (1662). La logique ou l’Art de penser. Paris: Chez Charles Savreux.

    Google Scholar 

  • Arnborg, S. (2006). Robust Bayesianism: Relation to evidence theory. Journal of Advances in Information Fusion, 1(1), 75–90.

    Google Scholar 

  • Arnborg, S., & Sjödin, G. (2000). Bayes rules in finite models. Proceedings of the European Conference on Artificial Intelligence, Berlin, pp. 571–575.

    Google Scholar 

  • Arnborg, S., & Sjödin, G. (2001). On the foundations of Bayesianism. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 20th International Workshop, American Institute of Physics, Gif-sur-Yvette, pp. 61–71.

    Google Scholar 

  • Ayton, P. (1997). How to be incoherent and seductive: Bookmaker’s odds and support theory. Organizational Behavior and Human Decision Processes, 72, 99–115.

    Google Scholar 

  • Bacon, F. (1605). The two books of Francis Bacon, of the proficience and advancement of learning, divine and humane. London: Henrie Tomes.

    Google Scholar 

  • Bacon, F. (1620). Novum Organum. London: J. Bill.

    Google Scholar 

  • Bacon, F. (1623). Historia vitae et mortis. Londini: In Officio Io. Haviland, impensis Matthei Lownes.

    Google Scholar 

  • Barbin, E., & Lamarche, J. P. (Eds.). (2004). Histoires de probabilités et de statistiques. Paris: Ellipses.

    Google Scholar 

  • Barbin, E., & Marec, Y. (1987). Les recherches sur la probabilité des jugements de Simon-Denis Poisson. Histoire & Mesure, 11(2), 39–58.

    Google Scholar 

  • Barbut, M. (1968). Les treillis des partitions d’un ensemble fini et leur représentation géométrique. Mathématiques et Sciences Humaines, 22, 5–22.

    Google Scholar 

  • Barbut, M. (2002). Une définition fonctionnelle de la dispersion en statistique et en calcul des probabilités: les fonctions de concentration de Paul Lévy. Mathématiques et Sciences Humaines, 40(158), 31–57.

    Google Scholar 

  • Barbut, M., & Monjardet, B. (1970). Ordre et classification. Algèbre et combinatoire. Tomes 1, 2. Paris: Librairie Hachette.

    Google Scholar 

  • Bartholomew, D. J. (1975). Probability and social science. International Social Science Journal, XXVII(3), 421–436.

    Google Scholar 

  • Bateman, B. W. (1987). Keynes changing conception of probability. Economics and Philosophy, 3(1), 97–120.

    Google Scholar 

  • Bayes, T. R. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.

    Google Scholar 

  • Bechtel, W., & Richardson, R. C. (1993). Discovering complexity. Princeton: Princeton University Press.

    Google Scholar 

  • Bellhouse, D. R. (2011) A new look at Halley’s life table. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(3), 823–832. The Royal Statistical Society Data Set Website: http://www.blackwellpublishing.com/rss/Readmefiles/A174p3bellhouse.htm. Accessed August 1, 2011.

  • Bentham, J. (1823). Traité des preuves judiciaires. Paris: Etienne Dumont.

    Google Scholar 

  • Benzécri, J. P., et collaborateurs. (1973). L’analyse des données (2 vols). Paris: Dunod.

    Google Scholar 

  • Berger, J. O., Bernardo, J. M., & Sun, D. (2009). The formal definition of reference priors. The Annals of Statistics, 37(2), 905–938.

    Google Scholar 

  • Berlinski, D. (1976). On systems analysis: An essay concerning the limitations of some mathematical methods in the social, political and biological sciences. Cambridge, MA: MIT Press.

    Google Scholar 

  • Bernardo, J. M. (2011). Integrated objective Bayesian estimation and hypothesis testing (with discussion). In J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, & M. West (Eds.), Bayesian statistics 9 (pp. 1–68). Oxford: Oxford University Press.

    Google Scholar 

  • Bernardo, J. M., & Smith, A. F. M. (1994). Bayesian theory. Chichester: Wiley.

    Google Scholar 

  • Bernoulli, N. (1709). Dissertatio inauguralis mathematico-juridica, de usu artis conjectandi in jure. Bâle.

    Google Scholar 

  • Bernoulli, J. I. (1713). Ars conjectandi. Bâle: Impensis Thurnisiorum fratrum.

    Google Scholar 

  • Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Commentarii Academiae Scientiarum Imperialis Petropolitanae, V, 175–192.

    Google Scholar 

  • Bernoulli, D. (1760). Essai d’une nouvelle analyse de la mortalité causée par la petite vérole, et des avantages de l’inoculation pour la prévenir. Mémoires de l’Académie Royale des Sciences de l’Année 1760, 1–45.

    Google Scholar 

  • Bernoulli, D. (1777). Diiudicatio maxime probabilis plurium observationum discrepantium atque verissimillima inductio inde formanda. Acta Academia Petropolitanae, pp. 3–23. (English translation by M. G. Kendall, Studies on the history of probability and statistics. XI. Daniel Bernoulli on maximum likelihood, Biometrika, 48(1), 1–18).

    Google Scholar 

  • Bernoulli, J. I., & Leibniz, G. W. (1692–1704). Lettres échangées (French translation by N. Meusnier (2006). Quelques échanges ? Journ@l électronique d’Histoire des Probabilités et de la Statistique, 2(1), 1–12).

    Google Scholar 

  • Bernstein, F. (1917). Oпыт aкcиoмaтичecкoгo oбocнoвaния тeopии вepoятнocтeй. Communication de la Société Mathématique de Karkov, 15, 209–274.

    Google Scholar 

  • Bertalanffy, L. (1968). General system theory. Foundations, development, applications. New York: John Braziler.

    Google Scholar 

  • Berthelot, J.-M. (Ed.). (2001). Épistémologie des sciences sociales. Paris: Presses Universitaires de France.

    Google Scholar 

  • Bertrand, J. (1889). Calcul des probabilités. Paris: Gauthier-Villard et Fils.

    Google Scholar 

  • Bhattacharya, S. K., Singh, N. K., & Tiwari, R. C. (1992). Hierarchical Bayesian survival analysis based on generalized exponential model. Metron, 50(3), 161–183.

    Google Scholar 

  • Bienaymé, J. (1838). Mémoire sur la probabilité des résultats moyens des observations; démonstration directe de la règle de Laplace. Mémoires Présentés à l’Académie Royale des Sciences de l’Institut de France, 5, 513–558.

    Google Scholar 

  • Bienaymé, J. (1855). Sur un principe que M. Poisson avait cru découvrir et qu’il avait appelé loi des grands nombres. Séances et travaux de l’Académie des sciences morales et politiques, 31, 379–389.

    Google Scholar 

  • Bienvenu, L., Shafer, G., & Shen, A. (2009). On the history of martingales in the study of randomness. Journ@l électronique d’Histoire des Probabilités et de la Statistique, 5(1), 1–40.

    Google Scholar 

  • Bijak, J. (2011). Forecasting international migration (Springer series on demographic methods and population analysis, Vol. 24). Dordrecht/Heidelberg/London/New York: Springer.

    Google Scholar 

  • Billari, F., & Prskawetz, A. (Eds.). (2003). Agent-based computational demography. Using simulation to improve our understanding of demographic behaviour. Heidelberg/New York: Physica-Verlag.

    Google Scholar 

  • Birkhoff, G. (1935). Abstract-linear dependence and lattices. American Journal of Mathematics, 57(4), 800–804.

    Google Scholar 

  • Blayo, C. (1995). La condition d’homogénéité en analyse démographique et en analyse statistique des biographies. Population, 50(6), 1501–1518.

    Google Scholar 

  • Bocquet-Appel, J.-P. (2005). La paléodémographie. In O. Dutour, J. J. Hublin, & B. Vandermeersch (Eds.), Objets et méthodes en paléoanthropologie (pp. 271–313). Paris: Comité des travaux historiques et scientifiques.

    Google Scholar 

  • Bocquet-Appel, J.-P., & Bacro, J.-N. (2008). Estimation of age distribution with its confidence intervals using an iterative Bayesian procedure and a bootstrap sampling approach. In J.-P. Bocquet-Appel (Ed.), Recent advances in paleodemography: Data, techniques, patterns (pp. 63–82). Dordrecht: Springer.

    Google Scholar 

  • Bocquet-Appel, J.-P., & Masset, C. (1982). Farewell to paleodemography. Journal of Human Evolution, 11, 321–333.

    Google Scholar 

  • Bocquet-Appel, J.-P., & Masset, C. (1996). Paleodemography: Expectancy and false hope. American Journal of Physical Anthropology, 99, 571–583.

    Google Scholar 

  • Bod, R., Hay, J., & Jannedy, S. (Eds.). (2003). Probabilistic linguistic. Cambridge, MA: MIT Press.

    Google Scholar 

  • Boisguilbert, P. (1695). Le Détail de la France, ou Traité de la cause de la cause de la diminution des biens, et des moyens d’y remédier. Rouen.

    Google Scholar 

  • Boltzmann, L. (1871). Einige allgemeine Sätze über wärmegleichgewicht. Sitzungberichte, K. Akademie der Wissenshaften, Wien, Mathematisch-Naturwissenchaftlichte Klasse, 63, 679–711.

    Google Scholar 

  • Bonneuil, N. (2004). Analyse critique de Pattern and repertoire in history, by Roehner B. and Syme, T. History and Theory, 43, 117–123.

    Google Scholar 

  • Bonvalet, C., Bry, X., & Lelièvre, E. (1997). Analyse biographique des groupes. Les avancées d’une recherche en cours. Population, 52(4), 803–830 (English translation (1998). Event history analysis of groups. The findings of an on going research project. Population, 10(1), 11–38).

    Google Scholar 

  • Boole, G. (1854). An investigation of the laws of thought: On which are founded the mathematical theories of logic and probability. London: Walton and Maberly.

    Google Scholar 

  • Bordas-Desmoulins, J.-B. (1843). Le cartésianisme ou la véritable rénovation des sciences. Paris: J. Hetzel.

    Google Scholar 

  • Borel, E. (1898). Leçons sur la théorie des fonctions. Paris: Gauthier-Villars et fils.

    Google Scholar 

  • Borel, E. (1909). Eléments de la théorie des probabilités. Paris: Librairie Hermann.

    Google Scholar 

  • Borel, E. (1914). Le hasard. Paris: Librairie Félix Alcan.

    Google Scholar 

  • Borsboom, D., Mellenberg, G. J., & van Herden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.

    Google Scholar 

  • Bourgeois-Pichat, J. (1994). La dynamique des populations. Populations stables, semi-stables, quasi-stables. Travaux et Documents, Cahier no. 133, Paris: INED/PUF.

    Google Scholar 

  • Braithwaite, R. B. (1941). Book review of Jeffreys’ Theory of probability. Mind, 50, 198–201.

    Google Scholar 

  • Bremaud, P. (1973). A martingale approach to point processes. Memorandum ERL-M345, Electronic research laboratory. Berkeley: University of California.

    Google Scholar 

  • Bretagnolle, J., & Huber-Carol, C. (1988). Effects of omitting covariates in Cox’s model for survival data. Scandinavian Journal of Statistics, 15, 125–138.

    Google Scholar 

  • Brian, E. (2001). Nouvel essai pour connaître la population du royaume. Histoire des sciences, calcul des probabilités et population de la France vers 1780. Annales de Démographie Historique, 102(2), 173–222.

    Google Scholar 

  • Brian, E. (2006). Les phénomènes sociaux que saisissait Jakob Bernoulli, aperçus de Condorcet à Auguste Comte. Journ@l Electronique d’Histoire des Probabilités et de la Statistique, 2(1b), 1–15.

    Google Scholar 

  • Brian, E., & Jaisson, M. (2007). The descent of human sex ratio at birth (Methodos series, Vol. 4). Dordrecht: Springer.

    Google Scholar 

  • Broggi, U. (1907). Die Axiome der Wahrscheinlichkeitsrechnung. PhD thesis, Universität Göttingen, Göttingen.

    Google Scholar 

  • Browne, W. J. (1998). Applying MCMC methods to multi-level models. PhD thesis, University of Bath, Bath, citeseerx.ist.edu: http://www.ams.ucsc.edu/~draper/browne-PhD-dissert. Accessed July 10, 2011.

  • Buck, C. E., Cavanagh, W. G., & Litton, W. G. (1996). Bayesian approach to interpreting archaeological data. Chichester: Wiley.

    Google Scholar 

  • Burch, T. (2002). Computer modelling of theory: Explanation for the 21st century. In R. Franck (Ed.), The explanatory power of models. Bridging the gap between empirical and theoretical models in the social sciences (Methodos series, Vol. 1, pp. 245–266). Boston/Dordrecht/London: Kluwer Academic Publishers.

    Google Scholar 

  • Burch, T. (2003). Data, models and theory: The structure of demographic knowledge. In F. C. Billari & A. Prskawetz (Eds.), Agent-based computational demography. Using simulation to improve our understanding of demographic behaviour (pp. 19–40). Heidelberg/New York: Physica-Verlag.

    Google Scholar 

  • Cantelli, F. P. (1905). Sui fondamenti del calcolo delle probabilità. Il Pitagora. Giornale di matematica per gli alunni delle scuole secondarie, 12, 21–25.

    Google Scholar 

  • Cantelli, F. P. (1932). Una teoria astratta del calcolo delle probabilità. Giornale dell’Istituto Italiano degli Attuari, 8, 257–265.

    Google Scholar 

  • Cantelli, F. P. (1935). Considérations sur la convergence dans le calcul des probabilités. Annales de l’I.H.P., 5(1), 3–50.

    Google Scholar 

  • Cantillon, R. (1755). Essai sur la nature du commerce en général. London: Fletcher Gyles.

    Google Scholar 

  • Cantor, G. (1873). Notes historiques sur le calcul des probabilités. In Comptes-rendus de la session de l’association de recherche scientifique, Halle, pp. 34–42.

    Google Scholar 

  • Cantor, G. (1874). Über eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen. Journal de Crelle, 77, 258–262.

    Google Scholar 

  • Cardano, J. (1663). Liber De Ludo Aleae. In Opera Omnia,Tomus I, Lugduni.

    Google Scholar 

  • Carnap, R. (1928). Der logische Aufbau der Welt. Leipzig: Felix Meiner Verlag (English translation 1967. The logical structure of the world. Pseudo problems in philosophy. Berkeley: University of California Press).

    Google Scholar 

  • Carnap, R. (1933). L’ancienne et la nouvelle logique. Paris: Hermann.

    Google Scholar 

  • Carnap, R. (1950). The logical foundations of probability. Chicago: University of Chicago Press.

    Google Scholar 

  • Carnap, R. (1952). The continuum of inductive methods. Chicago: University of Chicago Press.

    Google Scholar 

  • Carnot, S. (1824). Réflexions sur la puissance motrice du feu et les machines propres à développer cette puissance. Paris: Bachelier.

    Google Scholar 

  • Cartwright, N. (2006). Counterfactuals in economics: A commentary. In M. O’Rourke, J. K. Campbell, & H. Silverstein (Eds.), Explanation and causation: Topics in contemporary philosophy (pp. 191–221). Cambridge, MA: MIT Press.

    Google Scholar 

  • Cartwright, N. (2007). Hunting causes and using them: Approaches in philosophy and economics. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Cartwright, N. (2009). Causality, invariance and policy. In H. Kincaid & D. Ross (Eds.), The Oxford handbook of philosophy of economics (pp. 410–421). New York: Oxford University Press.

    Google Scholar 

  • Casini, L., Illari, P. M., Russo, F., & Williamson, J. (2011). Models for prediction, explanation and control: Recursive Bayesian networks. Theoria, 70, 5–33.

    Google Scholar 

  • Caticha, A. (2004). Relative entropy and inductive inference. In G. Erickson & Y. Zhai (Eds.), Bayesian inference and maximum entropy methods in science and engineering (Vol. 107, pp. 75–96). Melville: AIP.

    Google Scholar 

  • Caussinus, H., & Courgeau, D. (2010). Estimating age without measuring it: A new method in paleodemography. Population-E, 65(1), 117–144 (Estimer l’âge sans le mesurer en paléodémographie. Population, 65(1), 117–145).

    Google Scholar 

  • Caussinus, H., & Courgeau, D. (2011). Une nouvelle méthode d’estimation de la structure par âge des décès des adultes. In I. Séguy & L. Buchet (Eds.), Manuel de paléodémographie (pp. 291–325). Paris: INED.

    Google Scholar 

  • Charbit, Y. (2010). The classical foundations of population thought from Plato to Marx. Heidelberg/London: Springer.

    Google Scholar 

  • Chikuni, S. (1975). Biological study on the population of the Pacific Ocean perch in the North Pacific. Bulletin of the Far Seas Fisheries Research Laboratory, 12, 1–19.

    Google Scholar 

  • Choquet, G. (1953). Theory of capacities. Annales de l’Institut Fourier, 5, 131–296.

    Google Scholar 

  • Chung, K.-L. (1942). On mutually favourable events. The Annals of Statistics, 13, 338–349.

    Google Scholar 

  • Church, A. (1932). An unsolvable problem of elementary number theory. American Journal of Mathematics, 58(2), 345–363.

    Google Scholar 

  • Church, A. (1940). On the concept of a random sequence. Bulletin of the American Mathematical Society, 46(2), 130–135.

    Google Scholar 

  • Clausius, R. (1865). Ueber verschiedene für die Anwendung bequeme Formen der Hauptgleichungen der mechanischen Wärmetheorie. Annalen der Physik und Chemie, CXXV, 353–400.

    Google Scholar 

  • Clayton, D. (1991). A Monte-Carlo method for Bayesian inference in frailty models. Biometrics, 47, 467–485.

    Google Scholar 

  • Cohen, M. R., & Nagel, E. (1934). An introduction to logic and scientific method. New York: Harcourt Brace.

    Google Scholar 

  • Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32, 277–296.

    Google Scholar 

  • Colyvan, M. (2004). The philosophical significance of Cox’s theorem. International Journal of Approximate Reasoning, 37(1), 71–85.

    Google Scholar 

  • Colyvan, M. (2008). Is probability the only coherent approach to uncertainty? Risk Analysis, 28(3), 645–652.

    Google Scholar 

  • Comte, A. (1830–1842). Cours de philosophie positive. Paris: Bachelier.

    Google Scholar 

  • Condorcet. (1778). Sur les probabilités. Histoire de l’Académie Royale de Sciences, 1781, 43–46.

    Google Scholar 

  • Condorcet. (1785). Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. Paris: Imprimerie Nationale.

    Google Scholar 

  • Condorcet. (1786). Mémoire sur le calcul des probabilités. Cinquième partie. Sur la probabilité des faits extraordinaires. Mémoire pour l’Académie Royale des Sciences pour, 1783, pp. 553–559 (In Arithmétique politique. Textes rares ou inédits (1767–1789). Paris: Institut National d’Etudes Démographiques, pp. 431–436).

    Google Scholar 

  • Condorcet. (2004). In J.-P. Schandeler & P. Crépel (Eds.), Tableau historique des progrès de l’esprit humain. Projets, esquisse, fragments et notes (1772–1794). Paris: Institut National d’Etudes Démographiques.

    Google Scholar 

  • Copeland, A. (1928). Admissible numbers in the theory of probability. American Journal of Mathematics, 50, 535–552.

    Google Scholar 

  • Copeland, A. (1936). Point set theory applied to the random selection of the digits of an admissible number. American Journal of Mathematics, 58, 181–192.

    Google Scholar 

  • Copernic, N. (1543). De revolutionibus orbium coelestum. Nuremberg: Johanes Petreius ed.

    Google Scholar 

  • Coumet, E. (1970). La théorie du hasard est-elle née par hasard? Annales: Economies, Sociétés Civilisations, 25(3), 574–598.

    Google Scholar 

  • Coumet, E. (2003). Auguste Comte, le calcul des chances. Aberration radicale de l’esprit mathématique. Mathématiques et Sciences Humaines, 41(162), 9–17.

    Google Scholar 

  • Council of the Statistical Society of London. (1838). Introduction. Journal of the Statistical Society of London, 1(1), 1–5.

    Google Scholar 

  • Courgeau, D. (1982). Proposed analysis of the French Migration, family and occupation history survey. Multistate Life-History Analysis Task Force Meeting, Laxenburg: IIASA, pp. 1–14.

    Google Scholar 

  • Courgeau, D. (1985). Interaction between spatial mobility, family and career life-cycle: A French survey. European Sociological Review, 1(2), 139–162.

    Google Scholar 

  • Courgeau, D. (1991). Analyse de données biographiques erronées. Population, 46(1), 89–104.

    Google Scholar 

  • Courgeau, D. (1992). Impact of response error on event history analysis. Population: An English Selection, 4, 97–110.

    Google Scholar 

  • Courgeau, D. (2002). Evolution ou révolutions dans la pensée démographique? Mathématiques et Sciences Humaines, 40(160), 49–76.

    Google Scholar 

  • Courgeau, D. (Ed.). (2003). Methodology and epistemology of multilevel analysis. Approaches from different social sciences (Methodos series, Vol. 2). Dordrecht/Boston/London: Kluwer Academic Publishers.

    Google Scholar 

  • Courgeau, D. (2004a). Du groupe à l’individu. Synthèse multiniveau. Paris: INED.

    Google Scholar 

  • Courgeau, D. (2004b). Probabilités, démographie et sciences sociales. Mathématiques et Sciences Humaines, 42(3), 5–19.

    Google Scholar 

  • Courgeau, D. (2007a). Multilevel synthesis. From the group to the individual. Dordrecht: Springer.

    Google Scholar 

  • Courgeau, D. (2007b). Inférence statistique, échangeabilité et approche multiniveau. Mathématiques et Sciences Humaines, 45(179), 5–19.

    Google Scholar 

  • Courgeau, D. (2009). Paradigmes démographiques et cumulativité. In B. Walliser (Ed.), La cumulativité du savoir en sciences sociales (pp. 243–276). Lassay-les-Châteaux: Éditions de l’École des Hautes Études en Sciences Sociales.

    Google Scholar 

  • Courgeau, D. (2010). Dispersion of measurements in demography: A historical view. Electronic Journal for History of Probability and Statistics, 6(1), 1–19 (French version: La dispersion des mesures démographiques: vue historique. Journ@l Electronique d’Histoire des Probabilités et de la Statistique, 6(1), 1–20).

    Google Scholar 

  • Courgeau, D. (2011). Critiques des méthodes actuellement utilisés. In I. Séguy & L. Buchet (Eds.), Manuel de paléodémographie (pp. 255–290). Paris: INED.

    Google Scholar 

  • Courgeau, D., & Franck, R. (2007). Demography, a fully formed science or a science in the making. Population-E, 62(1), 39–45 (La démographie, science constituée ou en voie de constitution? Esquisse d’un programme. Population, 62(1), 39–46).

    Google Scholar 

  • Courgeau, D., & Lelièvre, E. (1986). Nuptialité et agriculture. Population, 41(2), 303–326.

    Google Scholar 

  • Courgeau, D., & Lelièvre, E. (1989). Analyse démographique des biographies. Paris: INED (English translation: (1992). Event history analysis in demography. Oxford: Clarendon Press. Spanish translation: (2001). Análisis demográfico de las biografías. México: El Colegio de México).

    Google Scholar 

  • Courgeau, D., & Lelièvre, E. (1996). Changement de paradigme en démographie. Population, 51(2), 645–654 (English translation: (1997) Changing paradigm in demography. Population. An English Selection, 9, 1–10).

    Google Scholar 

  • Courgeau, D., & Pumain, D. (1993). Spatial population issues. 6. France. In N. van Nimwegen, J.-C. Chesnais, & P. Dykstra (Eds.), Coping with sustained low fertility in France and the Netherlands (pp. 127–160). Amsterdam: Swetz & Zeitlinger Publishers.

    Google Scholar 

  • Cournot, A.-A. (1843). Exposition de la théorie des chances et des probabilités. Paris: Hachette.

    Google Scholar 

  • Cox, R. (1946). Probability, frequency, and reasonable expectation. American Journal of Physics, 14, 1–13.

    Google Scholar 

  • Cox, R. (1961). The algebra of probable inference. Baltimore: The John Hopkins Press.

    Google Scholar 

  • Cox, D. R. (1972). Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, 34(2), 187–220.

    Google Scholar 

  • Cox, D. R. (1975). Partial likelihood. Biometrika, 62, 269–276.

    Google Scholar 

  • Cox, R. (1979). On inference and inquiry. In R. D. Levine & M. Tribus (Eds.), The maximum entropy formalism (pp. 119–167). Cambridge, MA: MIT Press.

    Google Scholar 

  • Cox, D. R. (2006). Principles of statistical inference. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Cox, D. R., & Hinkley, D. V. (1974). Theoretical statistics. London: Chapman & Hall.

    Google Scholar 

  • Cox, D. R., & Oakes, D. (1984). Analysis of survival data. London/New York: Chapman & Hall.

    Google Scholar 

  • Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neurosciences. Oxford: Clarendon Press.

    Google Scholar 

  • Cribari-Neto, F., & Zarkos, S. G. (1999). Yet another econometric programming environment. Journal of Applied Econometrics, 14, 319–329.

    Google Scholar 

  • D’Alembert, J. l. R. (1761a). Réflexions sur le calcul des Probabilités. In Opuscules Mathématiques (Tome II, pp. 1–25). Dixième Mémoire, Paris: David.

    Google Scholar 

  • D’Alembert, J. l. R. (1761b). Sur l’application du calcul des probabilités à l’inoculation de la petite vérole. In Opuscules Mathématiques (Tome II, pp. 26–46). Onzième mémoire, Paris : David.

    Google Scholar 

  • D’Alembert, J. l. R. (1768a). Extrait de plusieurs lettres de l’auteur sur différents sujets, écrites dans le courant de l’année 1767. In Opuscules Mathématiques (Tome IV, pp. 61–105). Vingt-troisième mémoire, Paris: David.

    Google Scholar 

  • D’Alembert, J. l. R. (1768b). Extraits de lettres sur le calcul des probabilités et sur les calculs relatifs à l’inoculation. In Opuscules Mathématiques (Tome IV, pp. 283–341). Vingt-septième mémoire, Paris: David.

    Google Scholar 

  • Darden, L. (2002). Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward/backward chaining. Philosophy of Science (Supplement), 69, S354–S365.

    Google Scholar 

  • Darden, L. (Ed.). (2006). Reasoning in biological discoveries. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Daston, L. (1988). Classical probabilities in the enlightenment. Princeton: Princeton University.

    Google Scholar 

  • Daston, L. (1989). L’interprétation classique du calcul des probabilités. Annales: Economies, Sociétés, Civilisations, 44(3), 715–731.

    Google Scholar 

  • David, F. N. (1955). Studies in the history of probability and statistics. I. Dicing and gaming (A note on the history of probability). Biometrika, 42(1–2), 1–15.

    Google Scholar 

  • Davis, J. (2003). The relationship between Keynes‘s early and later philosophical thinking. In J. Runde & S. Mizuhara (Eds.), The philosophy of Keynes’s economics: Probability, uncertainty, and convention (pp. 100–110). London/New York: Routledge.

    Google Scholar 

  • Dawid, A. P. (2000). Causal inference without counterfactuals. Journal of the American Statistical Association, 95(450), 407–448.

    Google Scholar 

  • Dawid, A. P. (2007). Counterfactuals, hypothetical and potential responses: A philosophical examination of statistical causality. In F. Russo & J. Williamson (Eds.), Causality and probability in the sciences (Texts in philosophy series, Vol. 5, pp. 503–532). London: College Publications.

    Google Scholar 

  • Dawid, A. P., & Mortera, J. (1996). Coherent analysis of forensic identification evidence. Journal of the Royal Statistical Society, 58(2), 425–453.

    Google Scholar 

  • de Fermat, P. (1679). Varia opera mathemetica Petri de Fermat, Senatoris Tolosianis. Toulouse: Joannen Pech.

    Google Scholar 

  • de Finetti, B. (1931a). Sul significato soggettivo della probabilità. Fundamenta Mathematicae, 17, 298–329.

    Google Scholar 

  • de Finetti, B. (1931b). Sul concetto di media. Giornale dell’Instituto Italiano degli Attuari, 2, 367–396.

    Google Scholar 

  • de Finetti, B. (1937). La prévision: ses lois logiques, ses sources subjectives. Annales de l’Institut Henri Poincaré, 7(Paris), 1–68.

    Google Scholar 

  • de Finetti, B. (1951). Recent suggestions for the reconciliation of theories of probability. In J. Neyman (Ed.), Proceedings of the second Berkeley symposium on mathematical statistics and probability (pp. 217–225). Berkeley: University of California Press.

    Google Scholar 

  • de Finetti, B. (1952). Sulla preferibilità. Giornale degli Economisti e Annali de Economia, 11, 685–709.

    Google Scholar 

  • de Finetti, B. (1964). Foresight: Its logical laws, its subjective sources. In H. E. Kyburg & H. E. Smokler (Eds.), Studies in subjective probability (pp. 95–158). New York: Wiley.

    Google Scholar 

  • de Finetti, B. (1974). Theory of probability (2 vols). London/New York: Wiley.

    Google Scholar 

  • de Finetti, B. (1985). Cambridge probability theorists. The Manchester School, 53, 348–363.

    Google Scholar 

  • de Moivre, A. (1711). De mensura sortis, seu, de probabilitate eventuum in ludis a casu fortuito pendentibus. Philosophical Transactions, 27(329), 213–264.

    Google Scholar 

  • de Moivre, A. (1718). The doctrine of chances: Or a METHOD of calculating the probabilities of events in PLAY. London: Millar (Third edition: 1756).

    Google Scholar 

  • de Montessus, R. (1908). Leçons élémentaires sur le calcul des probabilités. Paris: Gauthier Villars.

    Google Scholar 

  • de Montmort, P. R. (1713). Essay d’analyse sur les jeux de hazard (2nd ed.). Paris: Jacques Quillau.

    Google Scholar 

  • Degenne, A., & Forsé, M. (1994). Les réseaux sociaux. Une approche structurale en sociologie. Paris: Armand Colin.

    Google Scholar 

  • Degenne, A., & Forsé, M. (1999). Introducing social networks. London: Sage.

    Google Scholar 

  • DeGroot, M. H. (1970). Optimal statistical decision. New York: McGraw-Hill.

    Google Scholar 

  • Delannoy, M. (1895). Sur une question de probabilités traitée par d’Alembert. Bulletin de la S.M.F., Tome 23, 262–265.

    Google Scholar 

  • Delaporte, P. (1941). Evolution de la mortalité en Europe depuis les origines des statistiques de l’état civil. Paris: Imprimerie Nationale.

    Google Scholar 

  • Dellacherie, C. (1978). Nombres au hasard de Borel à Martin Löf, Gazette des Mathématiques du Québec, 11, 1978. (Version remaniée de l’Institut de Mathématiques, Université Louis-Pasteur de Strasbourg (1978), 30 p).

    Google Scholar 

  • Demming, W. E. (1940). On a least squares adjustment of a sample frequency table when the expected marginal totals are known. Annals of Mathematical Statistics, 11, 427–444.

    Google Scholar 

  • Dempster, A. P. (1967). Upper and lower probabilities induced by a multilevel mapping. Annals of Mathematical Statistics, 38, 325–339.

    Google Scholar 

  • Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, Series B, 30, 205–245.

    Google Scholar 

  • Deparcieux, A. (1746). Essai sur les probabilités de la durée de la vie humaine. Paris: Frères Guerin.

    Google Scholar 

  • Descartes, R. (1647). Méditations, objections et réponses. In Œuvres et lettres. Paris: Gallimard.

    Google Scholar 

  • Desrosières, A. (1993). La politique des grands nombres: histoire de la raison statistique. Paris: La Découverte.

    Google Scholar 

  • Destutt de Tracy, A. L. C. (1801). Elémens d’idéologie (Vol. 4). Paris: Pierre Firmin Didot, An IX (Seconde édition (1804–1818)).

    Google Scholar 

  • Doob, J. L. (1940). Regularity properties of certain families of chance variables. Transactions of the American Mathematical Society, 44(1), 455–486.

    Google Scholar 

  • Doob, J. L. (1949). Application of the theory of martingales. In Actes du Colloque International: le Calcul des Probabilités et ses Applications (pp. 23–27). Paris: CNRS.

    Google Scholar 

  • Doob, J. L. (1953). Stochastic processes. New York: Wiley.

    Google Scholar 

  • Dormoy, E. (1874). Théorie mathématique des assurances sur la vie. Journal des Actuaires Français, 3, 283–299, 432–461.

    Google Scholar 

  • Draper, D. (1995). Inference and hierarchical modelling in the social sciences (with discussion). Journal of Educational and Behavioural Statistics, 20, 115–147, 233–239.

    Google Scholar 

  • Draper, D. (2008). Bayesian multilevel analysis and MCMC. In J. de Leeuw & E. Meyer (Eds.), Handbook of multilevel models (pp. 77–140). New York: Springer.

    Google Scholar 

  • Dubois, D., & Prade, H. (1988). An introduction to possibilistic and fuzzy logics. In P. Smets, A. Mandani, D. Dubois, & H. Prade (Eds.), Non standard logics for automated reasoning (pp. 287–326). New York: Academic.

    Google Scholar 

  • Duncan, W. J., & Collar, A. R. (1934). A method for the solution of oscillations problems by matrices. Philosophical Magazine, 17(Series 7), 865.

    Google Scholar 

  • Dupâquier, J. (1996). L’invention de la table de mortalité. Paris: Presses Universitaires de France.

    Google Scholar 

  • Durkheim, E. (1895). Les règles de la méthode sociologique. Paris: Alcan.

    Google Scholar 

  • Durkheim, E. (1897). Le suicide. Paris: Alcan.

    Google Scholar 

  • Dussause, H., & Pasquier, M. (1905). Les Œuvres Économiques de Sir William Petty (2 vols). Paris: Giard & Brière (French translation of The Economic Writings of Sir William Petty, edited by C. H. Hull (2 vols). Cambridge, MA: Cambridge University Press, 1899).

    Google Scholar 

  • Edgeworth, F. Y. (1883). The method of least squares. Philosophical Magazine, 5th Series, 16, 360–375.

    Google Scholar 

  • Edgeworth, F. Y. (1885a). Observation and statistics. An essay on the theory of errors of observation and the first principles of statistics. Transactions of the Cambridge Philosophical Society, 14, 138–169.

    Google Scholar 

  • Edgeworth, F. Y. (1885b). On methods of ascertaining variations in the rate of births, deaths and marriage. Journal of the Royal Statistical Society of London, 48, 628–649.

    Google Scholar 

  • Edgeworth, F. Y. (1892). Correlated averages. Philosophical Magazine, 5th Series, 34, 190–204.

    Google Scholar 

  • Edgeworth, F. Y. (1893a). Note on the correlation between organs. Philosophical Magazine, 5th Series, 36, 350–351.

    Google Scholar 

  • Edgeworth, F. Y. (1893b). Statistical correlations between social phenomena. Journal of the Royal Statistical Society, 56, 852–853.

    Google Scholar 

  • Edgeworth, F. Y. (1895). On some recent contributions to the theory of statistics. Journal of the Royal Statistical Society, 58, 506–515.

    Google Scholar 

  • Einstein, A. (1905). Zur Elektrodynamik bewegter Körper. Annalen der Physik, 332(10), 891–921.

    Google Scholar 

  • Ellis, R. L. (1849). On the Foundations of the theory of probability. Transactions of the Cambridge Philosophical Society, VIII, 1–16.

    Google Scholar 

  • Ellsberg, D. (1961). Risk, ambiguity and the Savage axioms. Quarterly Journal of Economics, 75(4), 643–669.

    Google Scholar 

  • Ellsberg, D. (2001). Risk, ambiguity and decision. New York/London: Garland Publishing Inc.

    Google Scholar 

  • Eriksson, L., & Hájek, A. (2007). What are degrees of belief? Studia Logica, 86, 185–215.

    Google Scholar 

  • Euler, L. (1760). Recherches générales sur la mortalité et la multiplication du genre humain. Histoire de l’Académie Royale des Sciences et des Belles Lettres de Berlin, 16, 144–164.

    Google Scholar 

  • Feller, W. (1934). Review of Kolmogorov (1933). Zentralblatt für Mathematik und ihre Grenzegebiete, 7, 216.

    Google Scholar 

  • Feller, W. (1950). An introduction to the theory of probability and its applications (Vol. 1). New York: Wiley.

    Google Scholar 

  • Feller, W. (1961). An introduction to the theory of probability and its applications (Vol. 2). New York: Wiley.

    Google Scholar 

  • Fergusson, T. S. (1973). A Bayesian analysis of some parametric problems. The Annals of Statistics, 1, 209–230.

    Google Scholar 

  • Fishburn, P. C. (1964). Decision and value theory. New York: Wiley.

    Google Scholar 

  • Fishburn, P. C. (1975). A theory of subjective probability and expected utilities. Theory and Decision, 6, 287–310.

    Google Scholar 

  • Fishburn, P. C. (1986). The axioms of subjective probability. Statistical Science, 1(3), 335–345.

    Google Scholar 

  • Fisher, R. A. (1922a). The mathematical theory of probability. London: Macmillan.

    Google Scholar 

  • Fisher, R. A. (1922b). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society, Series A, 222, 309–368.

    Google Scholar 

  • Fisher, R. A. (1923). Statistical tests of agreement between observation and hypothesis. Economica, 3, 139–147.

    Google Scholar 

  • Fisher, R. A. (1925a). Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society, 22, 700–725.

    Google Scholar 

  • Fisher, R. A. (1925b). Statistical methods for research workers. Edinburgh: Olivier and Boyd.

    Google Scholar 

  • Fisher, R. A. (1933). The concepts of inverse probability and fiducial probability referring to unknown parameters. Proceedings of the Royal Society, Series A, 139, 343–348.

    Google Scholar 

  • Fisher, R. A. (1934). Probability likelihood and quantity of information in the logic of uncertain inference. Proceedings of the Royal Society, Series A, 140, 1–8.

    Google Scholar 

  • Fisher, R. A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society, 98, 39–82.

    Google Scholar 

  • Fisher, R. A. (1956). Statistical methods and scientific inference. Edinburgh: Oliver and Boyd.

    Google Scholar 

  • Fisher, R. A. (1958). The nature of probability. Centennial Review, 2, 261–274.

    Google Scholar 

  • Fisher, R. A. (1960). Scientific thought and the refinement of human reasoning. Journal of the Operations Research Society of Japan, 3, 1–10.

    Google Scholar 

  • Florens, J.-P. (2002). Modèles de durée. In J.-J. Droesbeke, J. Fine, & G. Saporta (Eds.), Méthodes bayésiennes en statistique (pp. 315–330). Paris: Editions Technip.

    Google Scholar 

  • Florens, J. P., Mouchart, M., & Rolin, J.-M. (1999). Semi and non-parametric Bayesian analysis of duration models. International Statistical Review, 67(2), 187–210.

    Google Scholar 

  • Franck, R. (Ed.). (1994). Faut-il chercher aux causes une raison? L’explication causale dans les sciences humaines. Paris: Librairie Philosophique Vrin.

    Google Scholar 

  • Franck, R. (1995). Mosaïques, machines, organismes et sociétés. Examen métadisciplinaire du réductionnisme. Revue Philosophique de Louvain, 93, 67–81.

    Google Scholar 

  • Franck, R. (Ed.). (2002). The explanatory power of models. Bridging the gap between empirical and theoretical research in the social sciences. Boston/Dordrecht/London: Kluwer Academic Publishers.

    Google Scholar 

  • Franck, R. (2007). Peut-on accroître le pouvoir explicatif des modèles en économie? In A. Leroux, & P. Livet (dir.), Leçons de philosophie économique (Tome III, pp. 303–354). Paris: Economica.

    Google Scholar 

  • Franck, R. (2009). Allier l’investigation empirique et la recherché théorique: une priorité. In B. Walliser (Ed.), La cumulativité du savoir en sciences sociales (pp. 57–84). Lassay-les-Châteaux: Éditions de l’École des Hautes Études en Sciences Sociales.

    Google Scholar 

  • Franklin, J. (2001). Resurrecting logical probability. Erkenntnis, 55, 277–305.

    Google Scholar 

  • Fréchet, M. (1915). Sur l’intégrale d’une fonctionnelle étendue à un ensemble abstrait. Bulletin de la S.M.F., Tome 43, 248–265.

    Google Scholar 

  • Fréchet, M. (1937). Généralités sur le calcul des probabilités. Variables aléatoires. Paris: Gauthier Villars.

    Google Scholar 

  • Fréchet, M. (1938). Exposé et discussion de quelques recherches récentes sur les fondements du calcul des probabilités. In R. Wavre (Ed.), Les fondements du calcul des probabilités (Vol. II, pp. 23–55). Paris: Hermann.

    Google Scholar 

  • Fréchet, M. (1951). Rapport général sur les travaux du calcul des probabilités. In R. Bayer (Ed.), Congrès International de Philosophie des Sciences, Paris, 1949; IV: Calcul des probabilités (pp. 3–21). Paris: Hermann.

    Google Scholar 

  • Freund, J. E. (1965). Puzzle or paradox? The American Statistician, 19(4), 29–44.

    Google Scholar 

  • Fridriksson, A. (1934). On the calculation of age distribution within a stock of cods by means of relatively few age-determinations as a key to measurements on a large scale. Rapports et Procès-verbaux des Réunions du Conseil Permanent International pour l’Exploration des Mers, 86, 1–14.

    Google Scholar 

  • Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. The Journal of Political Economy, LVI(4), 279–304.

    Google Scholar 

  • Frischhoff, B., Slovic, B., & Lichteinstein, S. (1978). Fault trees: Sensitivity of estimated failure probabilities to problem representation. Journal of Experimental Psychology. Human Perception and Performance, 4, 330–344.

    Google Scholar 

  • Gacôgne, L. (1993). About a foundation of the Dempster’s rule, Rapport 93/27 Laforia.

    Google Scholar 

  • Gail, M., Wieand, S., & Piantadosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 71, 431–444.

    Google Scholar 

  • Galileo, G. (1613). Istoria e dimostrazioni intorno alle macchie solari e loro accidenti. Roma: Giacomo Mascardi.

    Google Scholar 

  • Galileo, G. (1898). Sopra le scoperte de i dadi. In Opere (Vol. VIII, pp. 591–594). Firenze: Barbera Editore.

    Google Scholar 

  • Galton, F. (1875). Statistics by intercomparison, with remarks on the law of frequency of error. Philosophical Magazine, 4th Series, 49, 33–46.

    Google Scholar 

  • Galton, F. (1886a). Family likeness in stature. Proceedings of the Royal Society of London, 40, 42–72 (Appendix by Hamilton Dickson, J. D., pp. 63–72).

    Google Scholar 

  • Galton, F. (1886b). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15, 246–263.

    Google Scholar 

  • Galton, F. (1888). Co-relations and their measurement, chiefly from anthropometric data. Proceedings of the Royal Society of London, 45, 135–145.

    Google Scholar 

  • Gärdenfors, P., Hansson, B., & Sahlin, N.-E. (Eds.). (1983). Evidential value: Philosophical, judicial and psychological aspects of a theory. Lund: Gleerups.

    Google Scholar 

  • Gardin, J.-C. (2002). The logicist analysis of explanatory theories in archæology. In R. Franck (Ed.), The explanatory power of models. Bridging the gap between empirical sciences and theoretical research in the social sciences (pp. 267–284). Boston/Dordrecht/London: Kluwer Academic Publishers.

    Google Scholar 

  • Garnett, J. C. M. (1919). On certain independent factors in mental measurements. Proceedings of the Royal Society London, Series A, 96, 91–111.

    Google Scholar 

  • Gauss, C. F. (1809). Theoria motus corporum celestium. Hamburg: Perthes et Besser.

    Google Scholar 

  • Gauss, C. F. (1816). Bestimmung der Genauigkeit der boebechtungen. Zeitshrifte für Astronomie und Verwandte Wissenschaften, 1, 185–216.

    Google Scholar 

  • Gauss, C. F. (1823). Theoria combinationis observationum erroribus minimis obnoxiae. Göttingen: Dieterich.

    Google Scholar 

  • Gavrilova, N. S., & Gavrilov, L. A. (2001). Mortality measurement and modeling beyond age 100. Living to 100 Symposium, Orlando, Florida. Website: http://www.soa.org/library/monographs/life/living-to-100/2011/mono-li11-5b-gavrilova.pdf. Accessed September 20, 2011.

  • Gelfand, A. E., & Solomon, H. (1973). A study of Poisson’s model for jury verdicts in criminal and civil trials. Journal of the American Statistical Association, 68(342), 271–278.

    Google Scholar 

  • Gelman, A., Karlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. New York: Chapman & Hall.

    Google Scholar 

  • Gergonne, J.-D. (1818–1819). Examen critique de quelques dispositions de notre code d’instruction criminelle. Annales de Mathématiques Pures et Appliquées (Annales de Gergonne), 9, 306–319.

    Google Scholar 

  • Gerrard, B. (2003). Keynesian uncertainty: What do we know? In J. Runde & S. Mizuhara (Eds.), The philosophy of Keynes’s economics: Probability, uncertainty, and convention (pp. 239–251). London/New York: Routledge.

    Google Scholar 

  • Ghosal, S. (1996). A review of consistency and convergence rates of posterior distributions. Proceedings of Varanashi Symposium in Bayesian Inference. India: Banaras Hindu University, pp. 1–10.

    Google Scholar 

  • Gibbs, J. W. (1902). Elementary principles in statistical mechanics. New Haven: Yale University Press.

    Google Scholar 

  • Gignac, G. E. (2007). Working memory and fluid intelligence are both identical to g?! Reanalyses and critical evaluation. Psychological Science, 49(3), 187–207.

    Google Scholar 

  • Gignac, G. E. (2008). Higher-order models versus direct hierarchical models: g as superordinate or breadth factor? Psychology Science Quarterly, 50(1), 21–43.

    Google Scholar 

  • Gill, J. (2008). Bayesian methods. A social and behavioral sciences approach. Boca Raton: Chapman & Hall.

    Google Scholar 

  • Gillies, D. (2000). Philosophical theories of probability. London/New York: Routledge.

    Google Scholar 

  • Gillies, D. (2003). Probability and uncertainty in Keynes’s economics. In J. Runde & S. Mizuhara (Eds.), The philosophy of Keynes’s economics: Probability, uncertainty, and convention (pp. 111–129). London/New York: Routledge.

    Google Scholar 

  • Gimblett, R. (Ed.). (2002). Integrating geographic information systems and agent-based modelling techniques for simulating social and ecological processes. New York: Oxford University Press.

    Google Scholar 

  • Gingerenzer, G., Swijtink, Z., Daston, L. J., Beatty, L., & Krüger, L. (Eds.). (1989). The empire of chance: How probability changed science and everyday life. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Glennan, S. (2002). Rethinking mechanical explanations. Philosophy of Science, 69(Proceedings), S342–S353.

    Google Scholar 

  • Glennan, S. (2005). Modeling mechanisms. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 375–388.

    Google Scholar 

  • Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. Monatshefte für Mathematik und Physik, 38, 173–198.

    Google Scholar 

  • Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least-squares. Biometrika, 73, 43–56.

    Google Scholar 

  • Goldstein, H. (1987). Multilevel covariance component models. Biometrika, 74, 430–431.

    Google Scholar 

  • Goldstein, H. (1991). Nonlinear multilevel models, with an application to discrete response data. Biometrika, 78, 45–51.

    Google Scholar 

  • Goldstein, H. (2003). Multilevel statistical models. London: Edward Arnold.

    Google Scholar 

  • Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality. Philosophical Transactions, 115, 513.

    Google Scholar 

  • Gonseth, F. (1975). Le référentiel, univers obligé de médiatisation. Lausanne: Editions l’Age d’Homme.

    Google Scholar 

  • Good, I. J. (1952). Rational decisions. Journal of the Royal Statistical Society, Series B, 14, 107–114.

    Google Scholar 

  • Good, I. J. (1956). Which comes first, probability or statistics? Journal of the Institute of Actuaries, 42, 249–255.

    Google Scholar 

  • Good, I. J. (1962). Subjective probability as the measure of a non-measurable set. In E. Nagel, P. Suppes, & A. Tarski (Eds.), Logic, methodology and philosophy of science (pp. 319–329). Stanford: Stanford University Press.

    Google Scholar 

  • Good, I. J. (1971). 46656 varieties of Bayesians. The American Statistician, 25, 62–63.

    Google Scholar 

  • Good, I. J. (1980). Some history of the hierarchical Bayesian methodology. In J. M. Bernardo et al. (Eds.), Bayesian statistics (pp. 489–519). Valencia: University of Valencia Press.

    Google Scholar 

  • Good, I. J. (1983). Good thinking. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Gosset, W. S. (Student) (1908a). The probable error of a mean. Biometrika, 6(1), 1–25.

    Google Scholar 

  • Gosset, W. S. (Student) (1908b). Probable error of a correlation coefficient. Biometrika, 6(2–3), 302–310.

    Google Scholar 

  • Gould, S. J. (1981). The mismeasure of man. New York: W.W. Norton & Co.

    Google Scholar 

  • Gouraud, C. (1848). Histoire du calcul des probabilités depuis ses origines jusqu’à nos jours. Paris: Auguste Durand.

    Google Scholar 

  • Graetzer, J. (1883). Edmund Halley und Caspar Neumann: Ein Beitrag zur Geschichte der Bevölkerungsstatistik. Breslau: Schottlaender.

    Google Scholar 

  • Granger, G.-G. (1967). Épistémologie économique. In J. Piaget (Ed.), Logique et connaissance scientifique (pp. 1019–1055). Paris: Editions Gallimard.

    Google Scholar 

  • Granger, G.-G. (1976). La théorie aristotélicienne de la science. Paris: Éditions Aubier Montaigne.

    Google Scholar 

  • Granger, G.-G. (1988). Essai d’une philosophie du style. Paris: Editions Odile Jacob.

    Google Scholar 

  • Granger, G.-G. (1992). A quoi sert l’épistémologie? Droit et Société, 20/21, 35–42.

    Google Scholar 

  • Granger, G.-G. (1994). Formes, opérations, objets. Paris: Librairie Philosophique Vrin.

    Google Scholar 

  • Graunt, J. (1662). Natural and political observations mentioned in a following index, and made upon the bills of mortality. London: Tho: Roycroft, for John Martin, James Allestry, and Tho: Dicas (French translation by Vilquin, E. (1977). Observations Naturelles et Politiques répertoriées dans l’index ci-après et faites sur les bulletins de mortalité. Paris: INED).

    Google Scholar 

  • Greenland, S. (1998a). Probability logic and probabilistic induction. Epidemiology, 9, 322–332.

    Google Scholar 

  • Greenland, S. (1998b). Induction versus Popper: Substance versus semantics. International Journal of Epidemiology, 27, 543–548.

    Google Scholar 

  • Greenland, S. (2000). Principles of multilevel modelling. International Journal of Epidemiology, 29, 158–167.

    Google Scholar 

  • Greenland, S., & Poole, C. (1988). Invariants and noninvariants in the concept of interdependent effects. Scandinavian Journal of Work, Environment and Health, 14, 125–129.

    Google Scholar 

  • Grether, D. M., & Plott, C. R. (1979). Economic theory of choice and the preference reversal phenomenon. The American Economic Review, 69(4), 623–638.

    Google Scholar 

  • Guillard, A. (1855). Eléments de statistique humaine ou démographie comparée. Paris: Guillaumin.

    Google Scholar 

  • Gurr, T. R. (1993). Minorities at risk: A global view of ethnopolitical conflicts. Washington, DC: United States Institute of Peace Progress.

    Google Scholar 

  • Gustafson, P. (1998). Flexible Bayesian modelling for survival data. Lifetime Data Analysis, 4, 281–299.

    Google Scholar 

  • Gustafsson, J.-E. (1984). A unifying model of the structure of intellectual abilities. Intelligence, 8, 179–203.

    Google Scholar 

  • Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1–12.

    Google Scholar 

  • Hacking, I. (1965). Logic of statistical inference. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hacking, I. (1975). The emergence of probability. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hacking, I. (1990). The taming of science. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hacking, I. (2001). An introduction to probability and inductive logic. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hadamard, J. (1922). Les principes du calcul des probabilités. Revue de Métaphysique et de Morale, 29(3), 289–293.

    Google Scholar 

  • Hájek, A. (2008a). Dutch book arguments. In P. Anand, P. Pattanaik, & C. Pup (Eds.), The Oxford handbook of corporate social responsibility, Phil papers: http://philrsss.anu.edu.au/people-defaults/alanh/papers/DBA.pdf. Accessed July 10, 2011.

  • Hájek, A. (2008b). Probability. In B. Gold (Ed.), Current issues in the philosophy of mathematics: From the perspective of mathematicians, Mathematical Association of America, Phil papers. http://philrsss.anu.edu.au/people-defaults/alanh/papers/overview.pdf. Accessed July 10, 2011.

  • Hald, A. (1990). A history of probability and statistics and their applications before 1750. New York: Wiley.

    Google Scholar 

  • Hald, A. (1998). A history of mathematical statistics from 1750 to 1930. New York: Wiley.

    Google Scholar 

  • Hald, A. (2007). A history of parametric statistical inference from Bernoulli to Fisher, 1713–1935. New York: Springer.

    Google Scholar 

  • Halley, E. (1693). An estimate of the degrees of the mortality of mankind, drawn from curious tables of the births and funeral’s at the City of Breslau; with an attempt to ascertain the price of the annuities upon lives. Philosophical Transactions Giving some Accounts of the Present Undertaking, Studies and Labour of the Ingenious in many Considerable Parts of the World, XVII(196), 596–610.

    Google Scholar 

  • Halpern, J. Y. (1999a). A counterexample of to theorems of Cox and Fine. The Journal of Artificial Intelligence Research, 10, 67–85.

    Google Scholar 

  • Halpern, J. Y. (1999b). Technical addendum, Cox’s theorem revisited. The Journal of Artificial Intelligence Research, 11, 429–435.

    Google Scholar 

  • Halpern, J. Y., & Koller, D. (1995). Representation dependence in probabilistic inference. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI’95), Montreal, Quebec, Canada, pp. 1853–1860.

    Google Scholar 

  • Halpern, J. Y., & Koller, D. (2004). Representation dependence in probabilistic inference. The Journal of Artificial Intelligence Research, 21, 319–356.

    Google Scholar 

  • Hanson, T. E. (2006). Modeling censored lifetime data using a mixture of Gamma baseline. Bayesian Analysis, 1(3), 575–594.

    Google Scholar 

  • Hanson, T. E., & Johnson, W. E. (2002). Modeling regression error with a mixture of Polya trees. Journal of the American Statistical Association, 97, 1020–1033.

    Google Scholar 

  • Harr, A. (1933). Der massbegrieff in der Theorie der kontinuierlichen Gruppen. Annals of Mathematical Statistics, 34, 147–169.

    Google Scholar 

  • Hasofer, A. M. (1967). Studies in the history of probability and statistics. XVI. Random mechanisms in Talmudic literature. Biometrika, 54(1/2), 316–321.

    Google Scholar 

  • Hasselblad, V. (1966). Estimation of parameters for a mixture of normal distribution. Technometrics, 8(3), 431–444.

    Google Scholar 

  • Hecht, J. (1977). L’idée de dénombrement jusqu’à la révolution. In Pour une histoire de la statistique (Vol. Tome 1/Contributions, pp. 21–81). Paris: INSEE.

    Google Scholar 

  • Heckman, J., & Singer, B. (1982). Population heterogeneity in demographic models. In K. Land & A. Rogers (Eds.), Multidimensional mathematical demography (pp. 567–599). New York: Academic.

    Google Scholar 

  • Heckman, J., & Singer, B. (1984a). Econometric duration analysis. Journal of Econometrics, 24, 63–132.

    Google Scholar 

  • Heckman, J., & Singer, B. (1984b). A method for minimizing distributional assumptions in econometric models for duration data. Econometrica, 52(2), 271–320.

    Google Scholar 

  • Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic explanation. Philosophy of Science, 15, 567–579.

    Google Scholar 

  • Henry, L. (1957). Un exemple de surestimation de la mortalité par la méthode de Halley. Population, 12(1), 141–142.

    Google Scholar 

  • Henry, L. (1959). D’un problème fondamental de l’analyse démographique. Population, 14(1), 9–32.

    Google Scholar 

  • Henry, L. (1966). Analyse et mesure des phénomènes démographiques par cohorte. Population, 13(1), 465–482.

    Google Scholar 

  • Henry, L. (1972). Démographie. Analyse et modèles. Paris: Larousse.

    Google Scholar 

  • Henry, L. (1981). Dictionnaire démographique multilingue. Liège: UIESP, Ordina éditions (English translation: Adapted by Van de Valle, E. (1982). Multilingual demographic dictionary. Liège: IUSSP, Ordina éditions).

    Google Scholar 

  • Henry, L., & Blayo, Y. (1975). La population de la France de 1740 à 1860. Population, 30, 71–122.

    Google Scholar 

  • Hespel, B. (1994). Revue sommaire des principales théories contemporaines de la causation. In R. Franck (Ed.), Faut-il chercher aux causes une raison ? L’explication causale dans les sciences humaines (pp. 223–231). Paris: Librairie Philosophique J. Vrin.

    Google Scholar 

  • Hilbert, D. (1902). Mathematical problems. Bulletin of the American Mathematical Society, 8(2), 437–439.

    Google Scholar 

  • Hoem, J. (1983). Multistate mathematical demography should adopt the notions of event history analysis. Stockholm Research Reports in Demography, 10, 1–17.

    Google Scholar 

  • Hofacker, J. D. (1829). Extrait d’une lettre du professeur Hofacker au rédacteur de la Gazette médico-chirurgicale d’Innsbruck. Annales d’hygiène publique et de médecine légale, 1(01), 557–558.

    Google Scholar 

  • Holland, P. (1986). Statistics and causal inference (with comments). Journal of the American Statistical Association, 81, 945–970.

    Google Scholar 

  • Hooper, G. (1699). A calculation of the credibility of human testimony. Anonymous translation in Philosophical Transactions of the Royal Society, 21, 359–365.

    Google Scholar 

  • Hooten, M. B., & Wilke, C. K. (2010). Statistical agent-based models for discrete spatio-temporal systems. Journal of the American Statistical Association, 105(489), 236–248.

    Google Scholar 

  • Hooten, M. B., Anderson, J., & Waller, L. A. (2010). Assessing North American influenza dynamics with a statistical SIRS model. Spatial and Spatio-temporal Epidemiology, 1, 177–185.

    Google Scholar 

  • Hoppa, R. D., & Vaupel, J. W. (Eds.). (2002). Paleodemography. Age distribution from skeletons samples. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Horn, J. L., & Catell, R. B. (1966). Refinement and test of the theory of fluid and crystallised general intelligence. Journal of Educational Psychology, 57, 253–270.

    Google Scholar 

  • Hunt, G. A. (1966). Martingales et processus de Markov. Paris: Dunod.

    Google Scholar 

  • Hunter, D. (1989). Causality and maximum entropy updating. International Journal of Approximate Reasoning, 3, 87–114.

    Google Scholar 

  • Hutter, M. (2001). Towards a universal theory of artificial intelligence based on an algorithmic probability and sequential decisions. In L. De Raedt & P. Flash (Eds.), Proceedings of the 12th European conference on machine learning (Lecture notes on artificial intelligence series). New York/Berlin/Heidelberg: Springer.

    Google Scholar 

  • Huygens, C. (1657). De ratiociniis in ludo aleae. Leyde: Elzevier.

    Google Scholar 

  • Huygens, C. (1895). Correspondance 1666–1669. In Œuvres complètes (Vol. Tome Sixième). La Haye: Martinus Nijhoff.

    Google Scholar 

  • Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian survival analysis. New York/Berlin/Heidelberg: Springer.

    Google Scholar 

  • Illari, P. M., & Williamson, J. (2010). Function and organization: Comparing the mechanisms of protein synthesis and natural selection. Studies in History and Philosophy of Biological and Biomedical Sciences, 41, 279–291.

    Google Scholar 

  • Illari, P. M., Russo, F., & Williamson, J. (Eds.). (2011). Causality in the sciences. Oxford: Oxford University Press.

    Google Scholar 

  • Inkelmann, F., Murrugarra, D., Jarrah, A. S., & Laubenbacher, R. (2010). A mathematical framework for agent based models of complex biological networks. ArXiv: 1006.0408v5 [q-bio.QM], 23 p.

    Google Scholar 

  • Irwin, J. O. (1941). Book review of Jeffreys’ Theory of probability. Journal of the Royal Statistical Society, 104, 59–64.

    Google Scholar 

  • Jacob, F. (1970). La logique du vivant. Paris: Gallimard.

    Google Scholar 

  • Jacob, P. (1980). L’empirisme logique: ses antécédents, ses critiques. Paris: Editions de Minuit.

    Google Scholar 

  • Jaynes, E. T. (1956). Probability theory in science and engineering. Dallas: Socony-Mobil Oil Co.

    Google Scholar 

  • Jaynes, E. T. (1957). How does brain do plausible reasoning? (Report 421), Microwave Laboratory, Stanford University (Published 1988, In G. J. Erickson, & C. R. Smith (Eds.), Maximum entropy and Bayesian methods in science and engineering (Vol. 1, pp. 1–24). Dordrecht: Kluwer Academic Publishers).

    Google Scholar 

  • Jaynes, E. T. (1963). Information theory and statistical mechanics. In K. Ford (Ed.), Statistical physics (pp. 181–218). New York: Benjamin.

    Google Scholar 

  • Jaynes, E. T. (1968). Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC-4, 227–241.

    Google Scholar 

  • Jaynes, E. T. (1976). Confidence intervals vs. Bayesian intervals. In R. G. Harper & G. Hooker (Eds.), Foundations of probability theory, statistical inference, and statistical theories of science (Vol. II, pp. 175–257). Dordrecht-Holland: D. Reidel Publishing Company.

    Google Scholar 

  • Jaynes, E. T. (1979). Where do we stand on maximum entropy? In R. D. Levine & M. Tribus (Eds.), The maximum entropy formalism (pp. 15–118). Cambridge, MA: MIT Press.

    Google Scholar 

  • Jaynes, E. T. (1980). Marginalization and prior probabilities. In A. Zellner (Ed.), Bayesian analysis in econometrics and statistics (pp. 43–87). Amsterdam: North Holland.

    Google Scholar 

  • Jaynes, E. T. (1988). How does the brain do plausible reasoning. In G. J. Erickson & C. R. Smith (Eds.), Maximum-entropy and Bayesian methods in science and engineering (Vol. 1, pp. 1–24). Dordrecht: Kluwer.

    Google Scholar 

  • Jaynes, E. T. (1990). Probability theory as logic. In P. F. Fougère (Ed.), Maximum entropy and Bayesian methods (pp. 1–16). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Jaynes, E. T. (1991). How should we use entropy in economics? Unpublished works by Edwin Jaynes. http://bayes.wustl.edu/etj/articles/entropy.in.economics.pdf. Accessed July 10, 2011.

  • Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Jeffreys, H. (1931). Scientific inference. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Jeffreys, H. (1932). On the theory of errors and least squares. Proceedings of the Royal Society, Series A, 138, 48–55.

    Google Scholar 

  • Jeffreys, H. (1933a). Probability, statistics, and the theory of errors. Proceedings of the Royal Society, Series A, 140, 523–535.

    Google Scholar 

  • Jeffreys, H. (1933b). On the prior probability in the theory of sampling. Proceedings of the Cambridge Philosophical Society, 29, 83–87.

    Google Scholar 

  • Jeffreys, H. (1934). Probability and scientific method. Proceedings of the Royal Society, Series A, 146, 9–16.

    Google Scholar 

  • Jeffreys, H. (1937). On the relation between direct and inverse methods in statistics. Proceedings of the Royal Society, Series A, 160, 325–348.

    Google Scholar 

  • Jeffreys, H. (1939). Theory of probability. New York: Clarendon Press.

    Google Scholar 

  • Jeffreys, H. (1946). An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London, Series A, 186, 453–461.

    Google Scholar 

  • Jeffreys, H. (1955). The present position in probability theory. The British Journal for the Philosophy of Science, 5, 275–289.

    Google Scholar 

  • Johnson, W. E. (1932). Probability: The deductive and inductive problem. Mind, 41, 409–423.

    Google Scholar 

  • Jones, K. (1993). ‘Everywhere is nowhere’: Multilevel perspectives on the importance of place. Portsmouth: The University of Portsmouth Inaugural Lectures.

    Google Scholar 

  • Jöreskog, K. G., & van Thillo, M. (1972). LISREL: A general computer program for estimating a linear structural equations system involving indicators of unmeasured variables. Princeton: Educational Testing Service. Educational Resources Information Center (ERIC). http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED073122&ERICExtSearch_SearchType_0=no&accno=ED073122. Accessed August 19, 2011.

  • Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgement of representativeness. Cognitive Psychology, 3, 430–454.

    Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.

    Google Scholar 

  • Kalbfleisch, J. D. (1978). Non-parametric Bayesian analysis of survival time data. Journal of the Royal Statistical Society, Series B, 40, 214–221.

    Google Scholar 

  • Kalbfleisch, J. D., & Prentice, R. L. (1980). The statistical analysis of failure time data. New York/Chichester/Brisbane/Toronto: Wiley.

    Google Scholar 

  • Kamke, E. (1932). Über neure Begründungen der Wahrscheinlichkeitsrechnung. Jahresbericht der Deutschen Mathematiker-Vereinigung, 42, 14–27.

    Google Scholar 

  • Kaplan, M. (1996). Decision theory as philosophy. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Kaplan, E. L., & Meier, P. (1958). Non-parametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457–481.

    Google Scholar 

  • Kardaun, O. J. W. F., Salomé, D., Schaafsma, W., Steerneman, A. G. M., Willems, J. C., & Cox, D. R. (2003). Reflections on fourteen cryptic issues concerning the nature of statistical inference. International Statistical Review, 71(2), 277–318.

    Google Scholar 

  • Karlis, D., & Patilea, V. (2007). Confidence hazard rate functions for discrete distributions using mixtures. Computational Statistics & Data Analysis, 51(11), 5388–5401.

    Google Scholar 

  • Kass, R. E., & Wassetman, L. (1996). The selection of prior distribution by formal rules. Journal of the American Statistical Association, 91, 1343–1369.

    Google Scholar 

  • Kaufmann, W. (1906). Über die Konstitution des Elektrons. Annalen der Physik, 324(3), 487–553.

    Google Scholar 

  • Keiding, N. (1990). Statistical inference in the Lexis diagram. Philosophical Transactions of the Royal Society of London, 332, 487–509.

    Google Scholar 

  • Kendall, M. G. (1956). Studies in the history of probability and statistics. II. The beginnings of a probability calculus. Biometrika, 43(1/2), 1–14.

    Google Scholar 

  • Kendall, M. G. (1960). Studies on the history of probability and statistics. X. Where shall the history of statistics begin? Biometrika, 47(3/4), 447–449.

    Google Scholar 

  • Kendall, M. G. (1963). Ronald Aylmer Fisher, 1890–1962. Biometrika, 50(1/2), 1–15.

    Google Scholar 

  • Kersseboom, W. (1742). Troisième traité sur la grandeur probable de la population de Hollande et de Frise occidentale. In Essais d’Arithmétique politique contenant trois traités sur la population de la province de Hollande et de Frise occidentale, Paris: Editions de l’Ined, 1970.

    Google Scholar 

  • Keynes, J. M. (1921). A treatise on probability. London: Macmillan.

    Google Scholar 

  • Keynes, J. M. (1971). Essay in biography. In The collected writings of John Maynard Keynes (Vol. X). London: Macmillan.

    Google Scholar 

  • Kimura, D. K. (1977). Statistical assessment of the age-length key. Journal of the Fisheries Research Board of Canada, 34, 317–324.

    Google Scholar 

  • Kimura, D. K., & Chikuni, S. (1987). Mixture of empirical distributions: An iterative application of the age-length key. Biometrics, 43, 23–35.

    Google Scholar 

  • Klotz, L. H. (1999). Is the rate of testicular cancer increasing? Canadian Medical Association Journal, 160, 213–214.

    Google Scholar 

  • Knuth, K. H. (2002). What is a question? In C. Williams (Ed.), Bayesian inference and maximum entropy methods in science and engineering, Moscow ID, 2002 (AIP conference proceedings, Vol. 659, pp. 227–242). Melville: American Institute of Physics.

    Google Scholar 

  • Knuth, K. H. (2003a). Intelligent machines in the twenty-first century: Foundations of inference and inquiry. Philosophical transactions of the Royal Society of London, Series A, 361, 2859–2873.

    Google Scholar 

  • Knuth, K. H. (2003b). Deriving laws from ordering relations. In G. J. Erickson & Y. Zhai (Eds.), Bayesian inference and maximum entropy methods in science and engineering, Jackson Hole WY, USA, 2003 (AIP conference proceedings, Vol. 707, pp. 204–235). Melville: American Institute of Physics.

    Google Scholar 

  • Knuth, K. H. (2005). Lattice duality: The origin of probability and entropy. Neurocomputing, 67, 245–274.

    Google Scholar 

  • Knuth, K. H. (2007). Lattice theory, measures, and probability. In K. H. Knuth, A. Caticha, J. L. Center, A. Giffin, & C. C. Rodríguez (Eds.), Bayesian inference and maximum entropy methods in science and engineering, Saratoga Springs, NY, USA, 2007 (AIP conference proceedings, Vol. 954, pp. 23–36). Melville: American Institute of Physics.

    Google Scholar 

  • Knuth, K. H. (2008). The origin of probability and entropy. In M. de Souza Lauretto, C. A. I. de Bragança Pereira, & J. M. Stern (Eds.), Bayesian inference and maximum entropy methods in science and engineering, Saõ Paulo, Brazil 2008 (AIP conference proceedings, Vol. 1073, pp. 35–48). Melville: American Institute of Physics.

    Google Scholar 

  • Knuth, K. H. (2009). Measuring on lattices. Arxiv preprint: arXiv0909.3684v1 (math..GM). Accessed July 11, 2011.

    Google Scholar 

  • Knuth, K. H. (2010a). Foundations of inference. Arxiv preprint: arXiv1008.4831v1 (math..PR). Accessed July 11, 2011.

    Google Scholar 

  • Knuth, K. H. (2010b). Information physics: The new frontier. Arxiv preprint: arXiv1009. 5161v1 (math.ph). Accessed July 11, 2011.

    Google Scholar 

  • Kolmogorov, A. (1933). Grundbegriffe der wahrscheinlichkeitsrenung. In Ergebisne der Mathematik (Vol. 2). Berlin: Springer (English translation, Morrison, N. (1950). Foundations of the theory of probability. New York: Chelsea).

    Google Scholar 

  • Kolmogorov, A. (1951). Bepoятнocть (Probability). In Great Soviet encyclopedia (Vol. 7, pp. 508–510). Moscow: Soviet Encyclopedia Publishing House.

    Google Scholar 

  • Kolmogorov, A. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1(1), 1–7.

    Google Scholar 

  • Konigsberg, L. W., & Frankenberg, S. R. (1992). Estimation of age structure in anthropological demography. American Journal of Physical Anthropology, 89, 235–256.

    Google Scholar 

  • Konigsberg, L. W., & Frankenberg, S. R. (2002). Deconstructing death in paleodemography. American Journal of Physical Anthropology, 117, 297–309.

    Google Scholar 

  • Konigsberg, L. W., & Herrmann, N. P. (2002). Markov Chain Monte Carlo estimation of hazard models parameters in paleodemography. In R. D. Hoppa & J. W. Vaupel (Eds.), Paleodemography. Age distribution from skeletons samples (pp. 222–242). Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Koopman, B. O. (1940). The axioms and algebra of intuitive probability. Annals of Mathematics, 41(2), 269–292.

    Google Scholar 

  • Koopman, B. O. (1941). Intuitive probabilities and sequences. Annals of Mathematics, 42(1), 169–187.

    Google Scholar 

  • Kraft, C. H., Pratt, J. W., & Seidenberg, A. (1959). Intuitive probability on finite sets. Annals of Mathematical Statistics, 30, 408–419.

    Google Scholar 

  • Krüger, L., Daston, L. J., & Heidelberg, M. (Eds.). (1986). The probabilistic revolution. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Kruithof, R. (1937). Telefoonverkeersreking. De Ingenieur, 52(8), E15–E25.

    Google Scholar 

  • Kuhn, T. (1962). The structure of scientific revolutions. Chicago/London: The University of Chicago Press.

    Google Scholar 

  • Kuhn, T. (1970). Postscript-1969. In The structure of scientific revolutions (2nd ed., pp. 174–210). Kuhn/Chicago/London: The University of Chicago Press.

    Google Scholar 

  • Kuhn, R., Everett, B., & Silvey, R. (2011). The effects of children’s migration on elderly kin’s health: A counterfactual approach. Demography, 48(1), 183–209.

    Google Scholar 

  • Kullbach, S., & Leiber, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.

    Google Scholar 

  • Kumar, D. (2010). Bayesian and hierarchical analysis of response-time data with concomitant variables. Journal of Biomedical Science and Engineering, 3, 711–718.

    Google Scholar 

  • Kyburg, H. (1978). Subjective probability: Criticisms, reflections and problems. Journal of Philosophical Logic, 7, 157–180.

    Google Scholar 

  • La Harpe, J.-F. (1799). Lycée, ou cours de littérature ancienne et moderne (Vol. 14). Paris: Chez H. Agasse (An VII).

    Google Scholar 

  • Laemmel, R. (1904). Untersuchungen über die Ermittlung von Wahrscheinlichkeiten. PhD thesis, Universität Zürich, Zurich.

    Google Scholar 

  • Lambert, J. H. (1764). Neues Organon oder gedanken über die Erforschung und Bezeichnung des wahren und dessen underscheidung von irrthum und schein. Leipzig: Johan Wendler.

    Google Scholar 

  • Lambert, J. H. (1772). Beyträge zum Gebrauche der Mathematik und deren Anwendung (Vol. III). Berlin: Verlag der Buchhandlung der Realschule.

    Google Scholar 

  • Landry, A. (1909). Les trois théories principales de la population. Scientia, 6, 1–29.

    Google Scholar 

  • Landry, A. (1945). Traité de démographie. Paris: Payot.

    Google Scholar 

  • Laplace, P. S. (1774). Mémoire sur la probabilité des causes par les événements. Mémoires de l’Académie Royale des Sciences de Paris, Tome VI, 621–656.

    Google Scholar 

  • Laplace, P. S. (1778). Mémoire sur les probabilités. Mémoires de l’Académie Royale des sciences de Paris, 1781, 227–332.

    Google Scholar 

  • Laplace, P. S. (1782). Mémoire sur les approximations des formules qui sont fonction de très grands nombres. Mémoires de l’Académie Royale des sciences de Paris, 1785, 1–88.

    Google Scholar 

  • Laplace, P. S. (1783a). Mémoire sur les approximations des formules qui sont fonctions de très-grands nombres (suite). Mémoires de l’Académie Royale des sciences de Paris, 1786, 423–467.

    Google Scholar 

  • Laplace, P. S. (1783b). Sur les naissances, les mariages et les morts à Paris, depuis 1771 jusqu’à 1784, et dans toute l’étendue de la France, pendant les années 1781 et 1782. Mémoires de l’Académie Royale des sciences de Paris, 1786, 693–702.

    Google Scholar 

  • Laplace, P. S. (1809a). Mémoire sur les approximations des formules qui sont fonction de très grands nombres et sur leur application aux probabilités. Mémoires de l’Académie Royale des sciences de Paris, 1810, 353–415.

    Google Scholar 

  • Laplace, P. S. (1809b). Supplément au mémoire sur les approximations des formules qui sont fonction de très grands nombres. Mémoires de l’Académie Royale des sciences de Paris, 1810, 559–565.

    Google Scholar 

  • Laplace, P. S. (1812). Théorie analytique des probabilités (Vols. 2). Paris: Courcier Imprimeur.

    Google Scholar 

  • Laplace, P. S. (1814). Essai philosophique sur les probabilités. Paris: Courcier Imprimeur.

    Google Scholar 

  • Laplace, P. S. (1816). Premier supplément sur l’application du calcul des probabilités à la philosophie naturelle. In Œuvres complètes (Vol. 13, pp. 497–530). Paris: Gauthier-Villars.

    Google Scholar 

  • Laplace, P. S. (1827). Mémoire sur le flux et le reflux lunaire atmosphérique. In Connaissance des Temps pour l’an 1830 (pp. 3–18). Paris: Veuve Coursier Imprimeur.

    Google Scholar 

  • Lazarsfeld, P. (Ed.). (1954). Mathematical thinking in the social science. Glencoe: The Free Press.

    Google Scholar 

  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Hougthton Mifflin.

    Google Scholar 

  • Lazarsfeld, P. F., & Menzel, H. (1961). On the relation between individual and collective properties. In A. Etzioni (Ed.), Complex organizations (pp. 422–440). New York: Holt, Reinhart and Winston.

    Google Scholar 

  • Le Bras, H. (1971). Géographie de la fécondité française depuis 1921. Population, 26(6), 1093–1124.

    Google Scholar 

  • Le Bras, H. (2000). Naissance de la mortalité. L’origine politique de la statistique et de la démographie. Paris: Seuil/Gallimard.

    Google Scholar 

  • Lebesgue, H. (1901). Sur une généralisation de l’intégrale définie. Comptes Rendus de l’Académie des Sciences, 132, 1025–1028.

    Google Scholar 

  • Lecoutre, B. (2004). Expérimentation, inférence statistique et analyse causale. Intellectica, 38(1), 193–245.

    Google Scholar 

  • Lee, P. M. (1989). Bayesian statistics. London: Arnold.

    Google Scholar 

  • Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Paris: Coursier.

    Google Scholar 

  • Legg, S. (1997). Solomonoff induction (Technical Report CDMTCS-030), Centre for Discrete Mathematics and Theoretical Computer Science, University of Auckland. http://www.vetta.org/documents/disSol.pdf. Accessed August 20, 2011.

  • Leibniz, G. W. (1666). Dissertatio de arte combinatoria. Leipzig (French translation: Peyroux, J. (1986). Dissertation sur l’art combinatoire. Paris: Blanchard).

    Google Scholar 

  • Leibniz, G. W. (1675). De problemata mortalitatis propositum per ducem de Roannez. Partie A du manuscrit traduite en français par M. Parmentier (1995). Paris: Librairie Philosophique Vrin.

    Google Scholar 

  • Leibniz, G. W. (1765). Nouveaux essais sur l’entendement humain. Paris: GF-Flammarion.

    Google Scholar 

  • Leibniz, G. W. (1995). L’estime des apparences: 21 manuscrits de Leibniz sur les probabilités, la théorie des jeux, l’espérance de vie, Texte établi, traduit, introduit et annoté par M. Parmentier. Paris: Librairie Philosophique Vrin.

    Google Scholar 

  • Leonard, T., & Hsu, S. J. (1999). Bayesian methods. An analysis for statisticians and interdisciplinary researchers. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Lévy, P. (1925). Calcul des probabilités. Paris: Gauthier-Villars.

    Google Scholar 

  • Lévy, P. (1936). Sur quelques points de la théorie des probabilités dénombrables. Annales de l’Institut Henri Poincaré, 6(2), 153–184.

    Google Scholar 

  • Lévy, P. (1937). Théorie de l’addition des variables aléatoires. Paris: Gauthier-Villars.

    Google Scholar 

  • Lewis, D. K. (1973a). Causation. The Journal of Philosophy, 70, 556–567.

    Google Scholar 

  • Lewis, D. K. (1973b). Counterfactuals. Oxford: Basil Blackwell.

    Google Scholar 

  • Lexis, W. (1877). Zur Theorie der Massenerscheinungen in der menschlichen Gesellschaft. Freiburg: Wagner.

    Google Scholar 

  • Lexis, W. (1879). Über die Theorie der Stabilität statistischer Reihen. Jahrbücher für Nationalökonomie und Statistik, 32, 60–98.

    Google Scholar 

  • Lexis, W. (1880). Sur les moyennes normales appliquées aux mouvements de la population et sur la vie normale. Annales de Démographie internationale, 4, 481–497.

    Google Scholar 

  • Lillard, L. A. (1993). Simultaneous equations fir hazards: Marriage duration and fertility timing. Journal of Econometrics, 56, 189–217.

    Google Scholar 

  • Lindley, D. V. (1956). On a measure of information provided by an experiment. Annals of Mathematical Statistics, 27(4), 986–1005.

    Google Scholar 

  • Lindley, D. V. (1962). Book review of the third edition of Jeffreys’ Theory of probability. Journal of the American Statistical Association, 57, 922–924.

    Google Scholar 

  • Lindley, D. V. (1977). A problem in forensic science. Biometrika, 64(2), 207–213.

    Google Scholar 

  • Lindley, D. V. (2000). The philosophy of statistics. Journal of the Royal Statistical Society, Series D (The Statistician), 49(3), 293–337.

    Google Scholar 

  • Lindley, D. V., & Novick, M. R. (1981). The role of exchangeability in inference. The Annals of Statistics, 9, 45–58.

    Google Scholar 

  • Lindley, D. V., & Smith, A. F. M. (1972). Bayes estimates for the linear model. Journal of the Royal Statistical Society, Series B (Methodological), 34, 1–41.

    Google Scholar 

  • Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. Hayward: Institute of Mathematical Statistics.

    Google Scholar 

  • Little, D. (2010). New contributions to the philosophy of history (Methodos series, Vol. 6). Dordrecht/Heidelberg/London/New York: Springer.

    Google Scholar 

  • Łomnicki, A. (1923). Nouveaux fondements du calcul des probabilités. Fundamenta Mathematicae, 4, 34–71.

    Google Scholar 

  • Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92, 805–824.

    Google Scholar 

  • Lotka, A. J. (1939). Théorie analytique des associations biologiques; Deuxième partie. Analyse démographique avec application particulière à l’espèce humaine. Paris: Herman.

    Google Scholar 

  • Louçã, F. (2007). The years of high econometrics: A short history of the generation that reinvented economics. London/New York: Routledge.

    Google Scholar 

  • Luce, R. D., & Krantz, D. H. (1971). Conditional expected utility. Econometrica, 39(2), 253–271.

    Google Scholar 

  • Luce, R. D., & Suppes, P. (1965). Preference, utility and subjective probability. In R. D. Luce, R. R. Bush, & E. H. Galanter (Eds.), Handbook of mathematical psychology (Vol. 3, pp. 249–410). New York: Wiley.

    Google Scholar 

  • Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.

    Google Scholar 

  • Machina, M. J. (1982). “Expected utility” analysis without the independence axiom. Econometrica, 50(2), 277–323.

    Google Scholar 

  • Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent based modelling. Annual Review of Sociology, 28, 143–166.

    Google Scholar 

  • Madden, E. H. (1969). A third view of causality. The Review of Metaphysics, XXIII, 67–84.

    Google Scholar 

  • Mandel, D. R. (2005). Are risk assessments of a terrorist attack coherent? Journal of Experimental Psychology Applied, 11, 277–288.

    Google Scholar 

  • Mandel, D. R. (2008). Violations of coherence in subjective probability: A representational and assessment processes account. Cognition, 106, 130–156.

    Google Scholar 

  • Manski, C. F., & McFadden, D. L. (1981). Structural analysis of discrete data and econometric applications. Cambridge, UK: The MIT Press.

    Google Scholar 

  • Manton, K. G., Singer, B., & Woodburry, M. A. (1992). Some issues in the quantitative characterization of heterogeneous populations. In J. Trussel, R. Hankinson, & J. Tilton (Eds.), Demographic applications of event history analysis (pp. 9–37). Oxford: Clarendon Press.

    Google Scholar 

  • March, L. (1908). Remarques sur la terminologie en statistique. In Congrès de mathématiques de Rome, JSSP, pp. 290–296.

    Google Scholar 

  • Martin, T. (Ed.). (2003). Arithmétique politique dans la France du XVIII e siècle. Classiques de l’économie et de la population. Paris: INED.

    Google Scholar 

  • Martin-Löf, P. (1966). The definition of random sequences. Information and Control, 7, 602–619.

    Google Scholar 

  • Masset, C. (1971). Erreurs systématiques dans la détermination de l’âge par les sutures crâniennes. Bulletins et Mémoires de la Société d’Anthropologie de Paris, 12(7), 85–105.

    Google Scholar 

  • Masset, C. (1982). Estimation de l’âge au décès par les sutures crâniennes. PhD thesis, University Paris VII, Paris.

    Google Scholar 

  • Masset, C. (1995). Paléodémographie: problèmes méthodologiques. Cahiers d’Anthropologie et Biométrie Humaine, XIII(1–2), 27–38.

    Google Scholar 

  • Masterman, M. (1970). The nature of a paradigm. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 59–89). Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Matalon, B. (1967). Epistémologie des probabilités. In J. Piaget (Ed.), Logique et connaissance scientifique (pp. 526–553). Paris: Gallimard.

    Google Scholar 

  • Maupin, M. G. (1895). Note sur une question de probabilités traitée par d’Alembert dans l’encyclopédie. Bulletin de la S.M.S., Tome 23, 185–190.

    Google Scholar 

  • Maxwell, J. C. (1860). Illustration of the dynamical theory of gases. The London, Edimburg, and Dubling Philosophical Magazine and Journal of Science, XIX, 19–32.

    Google Scholar 

  • McCrimmon, K., & Larson, S. (1979). Utility theory: Axioms versus “paradoxes”. In M. Allais & O. Hagen (Eds.), Expected utility hypotheses and the Allais paradox (pp. 333–409). Dordrecht: D. Reidel.

    Google Scholar 

  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115–133.

    Google Scholar 

  • McKinsey, J. C. C., Sugar, A., & Suppes, P. (1953). Axiomatic foundations of classical particle mechanics. Journal of Rational Mechanics and Analysis, 2, 273–289.

    Google Scholar 

  • McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.

    Google Scholar 

  • Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. W., III. (1972). The limits of growth. New York: Universe Books.

    Google Scholar 

  • Menken, J., & Trussel, J. (1981). Proportional hazards life table models: An illustrative analysis of socio-demographic influences on marriages and dissolution in the United States. Demography, 18(2), 181–200.

    Google Scholar 

  • Meusnier, N. (2004). Le problème des partis avant Pacioli. In E. Barbin & J.-P. Lamarche (Eds.), Histoires de probabilités et de statistiques (pp. 3–23). Paris: Ellipses.

    Google Scholar 

  • Meyer, P.-A. (1972). Martingales and stochastic integrals. Berlin/Heidelberg/New York: Springer.

    Google Scholar 

  • Mill, J. S. (1843). A system of logic, ratiocinate and inductive, being a connected view of the principles of evidence, and the methods of scientific investigation (Vol. II). London: Harrison and co.

    Google Scholar 

  • Missiakoulis, S. (2010). Cecrops, King of Athens: The first (?) recorded population census in history. International Statistical Review, 78(3), 413–418.

    Google Scholar 

  • Moheau, M. (1778). Recherches et considérations sur la population de la France. Edition annotée par Vilquin E., Paris: INED PUF.

    Google Scholar 

  • Mongin, P. (2003). L’axiomatisation et les théories économiques. Revue Economique, 54(1), 99–138.

    Google Scholar 

  • Morrison, D. (1967). On the consistency of preferences in Allais’s paradox. Behavioral Science, 12, 373–383.

    Google Scholar 

  • Mosteller, F., & Wallace, D. L. (1964). Applied Bayesian and classical inference: The case of the federalist papers. New York: Springer.

    Google Scholar 

  • Muliere, P., & Parmigiani, G. (1993). Utility and means in the 1930s. Statistical Science, 8(4), 421–432.

    Google Scholar 

  • Müller, H.-G., Love, B., & Hoppa, R. D. (2002). Semiparametric methods for estimating paleodemographic profiles from age indicator data. American Journal of Physical Anthropology, 117, 1–14.

    Google Scholar 

  • Nadeau, R. (1999). Vocabulaire technique et analytique de l’épistémologie. Paris: Presses Universitaires de France.

    Google Scholar 

  • Nagel, E. (1940). Book review of Jeffreys’ Theory of probability. The Journal of Philosophy, 37, 524–528.

    Google Scholar 

  • Nagel, E. (1961). The structure of science. London: Routledge and Kegan Paul.

    Google Scholar 

  • Narens, L. (1976). Utility, uncertainty and trade-off structures. Journal of Mathematical Psychology, 13, 296–332.

    Google Scholar 

  • Neuhaus, J. M., & Jewell, N. P. (1993). A geometric approach to assess bias due to omitted covariates in generalized linear models. Biometrika, 80(4), 807–815.

    Google Scholar 

  • Neveu, J. (1972). Martingales à temps discret. Paris: Masson.

    Google Scholar 

  • Newton, I. (1687). Philosophia naturalis principia mathematica. Londini: S. Pepys.

    Google Scholar 

  • Neyman, J. (1937). Outline of the theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society of London Series A, 236, 333–380.

    Google Scholar 

  • Neyman, J. (1940). Book review of Jeffreys’ Theory of probability. Journal of the American Statistical Association, 35, 558–559.

    Google Scholar 

  • Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Part I. Biometrika, 20A, 175–240.

    Google Scholar 

  • Neyman, J., & Pearson, E. S. (1933a). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, 231, 289–337.

    Google Scholar 

  • Neyman, J., & Pearson, E. S. (1933b). The testing of statistical hypotheses in relation to probabilities a priori. Proceedings of the Cambridge Philosophical Society, 26, 492–510.

    Google Scholar 

  • O’Donnel, R. (2003). The thick and the think of controversy. In J. Runde & S. Mizuhara (Eds.), The philosophy of Keynes’s economics: Probability, uncertainty, and convention (pp. 85–99). London/New York: Routledge.

    Google Scholar 

  • Orchard, T., & Woodbury, M. A. (1972). A missing information principle: Theory and applications. Proceedings of the VIth Berkeley Symposium on Mathematical Statistical Probability, 1, 697–715.

    Google Scholar 

  • Paris, J. B. (1994). The uncertain reasoner’s companion. A mathematical perspective. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Paris, J., & Vencovská, A. (1997). In defence of maximum entropy inference process. International Journal of Approximate Reasoning, 17(1), 77–103.

    Google Scholar 

  • Parlebas, P. (2002). Elementary mathematic modelization of games and sports. In R. Franck (Ed.), The explanatory power of models. Bridging the gap between empirical sciences and theoretical research in the social sciences (pp. 197–228). Boston/Dordrecht/London: Kluwer Academic Publishers.

    Google Scholar 

  • Pascal, B. (1640). Essay sur les coniques. B.N. Imp. Res. V 859.

    Google Scholar 

  • Pascal, B. (1645). Lettre dédicatoire à Monseigneur le Chancelier sur le sujet de la machine nouvellement inventée par le sieur B.P. pour faire toutes sortes d’opérations d’arithmétique par un mouvement réglé sans plume ni jetons avec un avis nécessaire à ceux qui auront la curiosité de voir ladite machine et s’en servir. In 1er manuscrit gros in 4° de Guerrier (archives de la famille Bellaigues de Bughas), pp. 721 et suiv.

    Google Scholar 

  • Pascal, B. (1648). Récit de l’expérience de l’équilibre des liqueurs. Paris: C. Savreux.

    Google Scholar 

  • Pascal, B. (1654a). Traité du triangle arithmétique, avec quelques autres traités sur le même sujet. Paris: Guillaume Desprez.

    Google Scholar 

  • Pascal, B. (1654b). Celeberrimæ mathesos academiæ Pariensi. Paris: Académie Parisienne.

    Google Scholar 

  • Pascal, B. (1670). Pensées. Paris: Édition de Port-Royal.

    Google Scholar 

  • Pascal, B. (1922). Les lettres de Blaise Pascal accompagnées de lettres de ses correspondants. Paris: Les Éditions G. Grès & Cie (Voir le courrier échangé avec Pierre de Fermat en 1654, pp. 188–229).

    Google Scholar 

  • Pasch, M. (1882). Vorlesungen über neure Geometrie. Leipzig: Verlag from Julius Springer.

    Google Scholar 

  • Patil, G. P., & Rao, C. R. (1994). Environmental statistics. Amsterdam: Elsevier Science B.V.

    Google Scholar 

  • Pearl, J. (1985). Bayesian networks: A model of self activated memory for evidential reasoning. Paper submitted to the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, 20 p.

    Google Scholar 

  • Pearl, J. (1988). Probabilistic reasoning and intelligent systems: Networks of plausible inference. San Mateo: Morgan Kaufmann.

    Google Scholar 

  • Pearl, J. (1995). Causal diagrams for empirical research (with discussion). Biometrika, 82(4), 669–710.

    Google Scholar 

  • Pearl, J. (2000). Causality, reasoning and inference. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Pearl, J. (2001). Bayesianism and causality, or, why I am only a half –Bayesian. In D. Corfield & J. Williamson (Eds.), Foundations of Bayesianism (Kluwer applied logic series, Vol. 24, pp. 19–36). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Pearson, K. (1894). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society of London, Series A, 185, 71–110.

    Google Scholar 

  • Pearson, K. (1896). Mathematical contributions to the theory of evolution, III: Regression, heredity and panmixia. Philosophical Transactions of the Royal Society of London, Series A, 187, 253–318.

    Google Scholar 

  • Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(5th series), 157–175.

    Google Scholar 

  • Pearson, K. (1911). The grammar of science. London: Adam and Charles Black.

    Google Scholar 

  • Pearson, K. (1920). The fundamental problem of practical statistics. Biometrika, 13(1), 1–16.

    Google Scholar 

  • Pearson, K. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika, 17(3/4), 388–442.

    Google Scholar 

  • Pearson, K., & Filon, L. N. G. (1898). Mathematical contributions to the theory of evolution, IV: On the probable errors of frequency constants and on the influence of random selection on variation and correlation. Philosophical Transactions of the Royal Society of London, Series A, 191, 229–311.

    Google Scholar 

  • Peirce, C. S. (1883). A theory of probable inference. In Studies in logic: Members of the John Hopkins University (pp. 126–203). Boston: Little, Brown and Company.

    Google Scholar 

  • Petty, W. (1690). Political arithmetick. London: Robert Clavel & Hen. Mortlock.

    Google Scholar 

  • Piaget, J. (1967). Les deux problèmes principaux de l’épistémologie des sciences de l’homme. In J. Piaget (Ed.), Logique et connaissance Scientifique (pp. 1114–1146). Paris: Gallimard.

    Google Scholar 

  • Plato. (around 360 B.C.). Laws. The Internet Classic Archive (B. Jowett, Trans.). Website: http://classics.mit.edu/Plato/laws.html. Accessed August 30, 2011.

  • Plato. (around 360 B.C.). Republic. The Internet Classic Archive (B. Jowett, Trans.). Website: http://classics.mit.edu/Plato/republic.html. Accessed August 30, 2011.

  • Plato. (around 360 B.C.). Stateman. The Internet Classic Archive (B. Jowett, Trans.). Website: http://classics.mit.edu/Plato/stateman.html. Accessed August 30, 2011.

  • Poincaré, H. (1912). Calcul des probabilités. Paris: Gauthier-Villars.

    Google Scholar 

  • Poinsot, L., & Dupin, C. (1836). Discussion de la « Note sur le calcul des probabilités » de Poisson. Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 2, 398–399.

    Google Scholar 

  • Poisson, S. D. (1835). Recherches sur la probabilité des jugements. Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 1, 473–474.

    Google Scholar 

  • Poisson, S. D. (1836a). Note sur la loi des grands nombres. Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 2, 377–382.

    Google Scholar 

  • Poisson, S. D. (1836b). Note sur le calcul des probabilités. Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 2, 395–399.

    Google Scholar 

  • Poisson, S.-D. (1837). Recherches sur la probabilité des jugements en matière criminelle et en matière civile. Paris: Bachelier.

    Google Scholar 

  • Polya, G. (1954). Mathematics and plausible reasoning (2 vols). Princeton: Princeton University Press.

    Google Scholar 

  • Popper, K. (1934). Logik der forshung. Vienna: Springer.

    Google Scholar 

  • Popper, K. (1956). Adequacy and consistency: A second reply to Dr. Bar Illel. The British Journal for the Philosophy of Science, 7, 249–256.

    Google Scholar 

  • Popper, K. (1959). The propensity interpretation of probability. Philosophy of Science, 10, 25–42.

    Google Scholar 

  • Popper, K. (1982). The postscript to The logic of scientific discovery III. Quantum theory and the schism in physics. London: Hutchinson.

    Google Scholar 

  • Popper, K. (1983). The postscript of the logic of scientific discovery. I. Realism and the aim of science. London: Hutchinson.

    Google Scholar 

  • Porter, T. M. (1986). The rise of statistical thinking 1820–1900. Princeton: Princeton University Press.

    Google Scholar 

  • Poulain, M., Riandey, B., & Firdion, J. M. (1991). Enquête biographique et registre belge de population: une confrontation des données. Population, 46(1), 65–88.

    Google Scholar 

  • Poulain, M., Riandey, B., & Firdion, J. M. (1992). Data from a life history survey and the Belgian population register: A comparison. Population: An English Selection, 4, 77–96.

    Google Scholar 

  • Pratt, D. (2010). Modeling written communication. A new systems approach to modelling in the social sciences. Dordrecht/Heidelberg/London/New York: Springer.

    Google Scholar 

  • Prentice, R. L. (1978). Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika, 69, 167–179.

    Google Scholar 

  • Pressat, R. (1966). Principes d’analyse. Paris: INED.

    Google Scholar 

  • Preston, M. G., & Baratta, P. (1948). An experimental study of the auction value of an uncertain outcome. The American Journal of Psychology, 61, 183–193.

    Google Scholar 

  • Preston, S. H., & Coale, A. J. (1982). Age/structure growth, attrition and accession: A new synthesis. Population Index, 48(2), 217–259.

    Google Scholar 

  • Quesnay, F. (1758). Tableau oeconomique (Document M 784 no. 71-1). Paris: Archives Nationales (Published in (2005) C. Théré, L. Charles, J.-C. Perrot (Eds.), Œuvres économiques complètes et autres textes de François Quesnay. Paris: INED).

    Google Scholar 

  • Quetelet, A. (1827). Recherches sur la population, les naissances, les décès, les prisons, les dépôts de mendicité, etc., dans le royaume des Pays-Bas. Nouveaux mémoires de l’académie royale des sciences et des belles-lettres de Bruxelles, 4, 117–192.

    Google Scholar 

  • Quetelet, A. (1835). Sur l’homme et le développement de ses facultés, ou Essai de physique sociale. Tome premier et Tome second. Paris: Bachelier.

    Google Scholar 

  • Rabinovitch, N. L. (1969). Studies in the history of probability and statistics. XXII. Probability in the Talmud. Biometrika, 56(2), 437–441.

    Google Scholar 

  • Rabinovitch, N. L. (1970). Studies on the history of probability and statistics. XXIV. Combinations and probability in rabbinic literature. Biometrika, 57(1), 203–205.

    Google Scholar 

  • Radon, J. (1913). Theorie und Anwendungen der absolut Additiven Mengenfunktionen. Sitzungsberichte der kaiserlichen Akademie der Wissenschaften, Mathematisch-Naturwissenschaftliche Klasse 122 IIa , pp. 1295–1438.

    Google Scholar 

  • Railton, P. (1978). A deductive-nomological model of probabilistic explanation. Philosophy of Science, 45, 206–226.

    Google Scholar 

  • Ramsey, F. P. (1922). Mr. Keynes and probability. The Cambridge Magazine, 11(1), 3–5.

    Google Scholar 

  • Ramsey, F. P. (1926). Truth and probability. In F. P. Ramsey. (1931). The foundations of mathematics and other logical essays, R. B. Braithwaite (Ed.), Chapter VII, pp. 156–198. London: Kegan, Trubner & Co., New York: Harcourt, Brace and Company.

    Google Scholar 

  • Ramsey, F. P. (1931). In R. B. Braithwaite (Ed.), The foundations of mathematics and other logical essays. London/New York: Kegan, Trubner & Co/Harcourt, Brace and Company.

    Google Scholar 

  • Reeves, J. (1987). Projection of number of kin. In J. Bongaarts, T. Burch, & K. Wachter (Eds.), Family demography (pp. 228–248). Oxford: The Clarendon Press.

    Google Scholar 

  • Reichenbach, H. (1935). Wahrscheinlichkeitslehre : eine Untersuchung über die logischen und mathematischen Grundlagen der Wahrscheinlichkeitsrechnung. Leiden: Sijthoff (English translation: 2nd edition (1949) The theory of probability, an inquiry into the logical and mathematical foundations of the calculus of probability, Berkeley-Los Angeles: University of California Press).

    Google Scholar 

  • Reichenbach, H. (1937). Les fondements logiques du calcul des probabilités. Annales de l’Institut Henri Poincaré, 7, 267–348.

    Google Scholar 

  • Remenik, D. (2009). Limit theorems for individual-based models in economics and finance. arXiv: 0812813v4 [math.PR], 38 p.

    Google Scholar 

  • Reungoat, S. (2004). William Petty. Observateur des Îles Britanniques. Classiques de l’économie et de la population. Paris: INED.

    Google Scholar 

  • Richardson, S., & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society, Series B, 59(4), 731–792.

    Google Scholar 

  • Ripley, B. D. (1994). Neural networks and related methods for classification. Journal of the Royal Statistical Society, Series B, 56(3), 409–456.

    Google Scholar 

  • Ripley, R. M. (1998). Neural networks models for breast cancer prognoses. Doctor of Philosophy thesis. Oxford: Oxford University, http://portal.stats.ox.ac.uk/userdata/ruth/thesis.pdf. Accessed July 11, 2011.

  • Ripley, B. D., & Ripley, R. M. (1998). Neural networks as statistical methods in survival analysis. In R. Dybrowski & V. Gant (Eds.), Artificial neural networks: Prospects for medicine. Austin: Landes Biosciences Publishers.

    Google Scholar 

  • Robert, C. P. (2006). Le choix bayésien. Paris: Springer.

    Google Scholar 

  • Robert, C. P., Chopin, N., & Rousseau, J. (2009). Harold Jeffreys’s theory of probability revisited. Statistical Science, 24(2), 141–172.

    Google Scholar 

  • Roberts, H. V. (1974). Reporting of Bayesian studies. In S. E. Fienberg & A. Zellner (Eds.), Studies in Bayesian econometrics and statistics: In honor of Leonard J. Savage (pp. 465–483). Amsterdam: North Holland.

    Google Scholar 

  • Robertson, B., & Vignaux, G. A. (1991). Inferring beyond reasonable doubt. Oxford Journal of Legal Studies, 11(3), 431–438.

    Google Scholar 

  • Robertson, B., & Vignaux, G. A. (1993). Probability – The logic of the law. Oxford Journal of Legal Studies, 13(4), 457–478.

    Google Scholar 

  • Robertson, B., & Vignaux, G. A. (1995). Interpreting evidence: Evaluation forensic science in the courtroom. New York/Chichester/Brisbane/Toronto: Wiley.

    Google Scholar 

  • Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357.

    Google Scholar 

  • Roehner, B., & Syme, T. (2002). Pattern and repertoire in history. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Rohrbasser, J.-M. (2002). Qui a peur de l’arithmétique? Les premiers essais de calcul sur les populations dans la seconde moitié du XVIIe siècle. Mathématiques et Sciences Humaines, 159, 7–41.

    Google Scholar 

  • Rohrbasser, J.-M., & Véron, J. (2001). Leibniz et les raisonnements sur la vie humaine. Classiques de l’économie et de la population. Paris: INED.

    Google Scholar 

  • Rouanet, H., Bernard, J.-M., Bert, M.-C., Lecoutre, B., Lecoutre, M.-P., & Le Roux, B. (1998). New ways in statistical methodology. From significance tests to Bayesian inference. Bern: Peter Lang.

    Google Scholar 

  • Rouanet, H., Lebaron, F., Le Hay, V., Ackermann, W., & Le Roux, B. (2002). Régression et analyse géométrique des données: réflexions et suggestions. Mathématiques et Sciences Humaines, 160, 13–45.

    Google Scholar 

  • Royall, R. M. (1970). On finite population theory under certain linear regression models. Biometrika, 57(2), 377–387.

    Google Scholar 

  • Rubin, D. B. (1974). Estimating the causal effects of treatments in randomized and non randomized studies. Journal of Educational Psychology, 66, 688–701.

    Google Scholar 

  • Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Psychology, 2, 1–26.

    Google Scholar 

  • Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6, 34–58.

    Google Scholar 

  • Russo, F. (2009). Causality and causal modelling in the social sciences. Measuring variations (Methodos series, Vol. 5). Dordrecht: Springer.

    Google Scholar 

  • Ryder, N. B. (1951). The cohort approach. Essays in the measurement of temporal variations in demographic behaviour. PhD dissertation. New York: Princeton University.

    Google Scholar 

  • Ryder, N. B. (1954). La mesure des variations de la fécondité au cours du temps. Population, 11(1), 29–46.

    Google Scholar 

  • Ryder, N. B. (1964). Notes on the concept of population. The American Journal of Sociology, 69(5), 447–463.

    Google Scholar 

  • Sadler, M. T. (1830). The law of population. A treatise, in six books, in disproof of the superfecondity of human beings, and developing the real principle of their increase. London: Murray.

    Google Scholar 

  • Salmon, W. C. (1961). Vindication of induction. In H. Feigl & G. Maxwell (Eds.), Current issues in the philosophy of science (pp. 245–264). New York: Holt, Reinhart, and Winston.

    Google Scholar 

  • Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.

    Google Scholar 

  • Salmon, W. C. (1991). Hans Reichenbach’s vindication of induction. Erkenntnis, 35(1–3), 99–122.

    Google Scholar 

  • Sarkar, S. (Ed.). (1996). Decline and obsolescence of logical empirism: Carnap vs. Quine and the critics. New York/London: Garland Publishing, Inc.

    Google Scholar 

  • Savage, L. J. (1954). The foundations of statistics. New York: Wiley.

    Google Scholar 

  • Savage, L. J. (1962). The foundations of statistical inference. New York: Wiley.

    Google Scholar 

  • Savage, L. J. (1967). Implications of personal probability for induction. The Journal of Philosophy, 64(19), 593–607.

    Google Scholar 

  • Savage, L. J. (1976). On reading R.A. Fisher. The Annals of Statistics, 4(3), 441–500.

    Google Scholar 

  • Schmid, J., & Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 83–90.

    Google Scholar 

  • Schmitt, R. C., & Crosetti, A. H. (1954). Accuracy of the ratio-correlation method for estimating postcensal population. Land Economics, 30, 279–281.

    Google Scholar 

  • Scott, D. (1964). Measurement structures and linear inequalities. Journal of Mathematical Psychology, 1, 233–247.

    Google Scholar 

  • Séguy, I., & Buchet, L. (Eds.). (2011). Manuel de paléodémographie. Paris: INED.

    Google Scholar 

  • Seidenfeld, T. (1987). Entropy and uncertainty. In I. B. MacNeill & G. J. Umphrey (Eds.), Foundations of statistical inference (pp. 259–287). Boston: Reidel.

    Google Scholar 

  • Seidenfeld, T., Schervish, M. J., & Kadane, J. B. (1990). When fair betting odds are not degrees of belief. Philosophical Science Association, 1, 517–524.

    Google Scholar 

  • Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.

    Google Scholar 

  • Shafer, G. (1979). Allocations of probability. Annals of Probability, 7(5), 827–839.

    Google Scholar 

  • Shafer, G. (1982). Thomas Bayes’s Bayesian inference. Journal of the Royal Statistical Society, Series A, 145(2), 250–258.

    Google Scholar 

  • Shafer, G. (1985). Conditional probability. International Statistical Review, 53(3), 261–277.

    Google Scholar 

  • Shafer, G. (1986). Savage revisited. Statistical Science, 1(4), 463–501.

    Google Scholar 

  • Shafer, G. (1990a). The unity and diversity of probability (with comments). Statistical Science, 5(4), 435–462.

    Google Scholar 

  • Shafer, G. (1990b). The unity of probability. In G. M. von Furstenberg (Ed.), Acting under uncertainty: Multidisciplinary conceptions (pp. 95–126). New York: Kluwer.

    Google Scholar 

  • Shafer, G. (1992). What is probability? In D. C. Hoaglin & D. S. Moore (Eds.), Perspectives on contemporary statistics (pp. 93–106). New York: Mathematical Association of America.

    Google Scholar 

  • Shafer, G. (1996). The art of causal conjecture. Cambridge, MA: MIT Press.

    Google Scholar 

  • Shafer, G. (2001). The notion of event in probability and causality. Situating myself relative to Bruno de Finetti. Unpublished paper, presented in Pisa and in Bologna march 2001, 14 p.

    Google Scholar 

  • Shafer, G. (2004). Comments on “Constructing a logic of plausible inference: A guide to Cox’s theorem”, by Kevin S. Van Horn. International Journal of Approximate Reasoning, 35, 97–105.

    Google Scholar 

  • Shafer, G. (2010). A betting interpretation for probabilities and Dempster-Shafer degrees of belief (Working paper 31), Project website: http://probabilityandfinance.com, 18 p. Accessed July 11, 2011.

  • Shafer, G., & Pearl, J. (Eds.). (1990). Readings in uncertainty reasoning. San Mateo: Morgan Kaufman Publishers.

    Google Scholar 

  • Shafer, G., & Volk, V. (2006). The sources of Kolmogorov Grundbegriffe. Statistical Science, 21(1), 70–98.

    Google Scholar 

  • Shafer, G., & Vovk, V. (2001). Probability and finance: It’s only a game! New York: Wiley.

    Google Scholar 

  • Shafer, G., & Vovk, V. (2005). The origins and legacy of Kolmogorov’s Grundbegriffe (Working paper 4), Project web site: http://probabilityandfinance.com, 104 p. Accessed July 11, 2011.

  • Shafer, G., Gilett, P. R., & Scherl, R. B. (2000). The logic of events. Annals of Mathematics and Artificial Intelligence, 28, 315–389.

    Google Scholar 

  • Shalizi, C. R. (2009). Dynamics of Bayesian updating with dependent data and misspecified models. Electronic Journal of Statistics, 3, 1039–1074.

    Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.

    Google Scholar 

  • Shore, J. E., & Johnson, R. W. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory, IT-26, 26.

    Google Scholar 

  • Simpson, E. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B, 13, 238–241.

    Google Scholar 

  • Sinha, D. (1993). Semiparametric Bayesian analysis of multiple even time data. Journal of the American Statistical Association, 88(423), 979–983.

    Google Scholar 

  • Skilling, J. (1988). The axioms of maximum entropy. In G. J. Erickson & C. R. Smith (Eds.), Maximum entropy and Bayesian methods in science and engineering (Vol. 1, pp. 173–188). Boston/Dordrecht/London: Kluwer.

    Google Scholar 

  • Skilling, J. (1998). Probabilistic data analysis: An introductory guide. Journal of Microscopy, 190(1–2), 28–36.

    Google Scholar 

  • Slovic, P., & Tversky, A. (1974). Who accepts Savage’s axioms? Behavioral Science, 19, 368–373.

    Google Scholar 

  • Slutsky, E. (1922). К вoпpocу o лoгичecкиx ocнoвax тeopии вepoятнocти (Sur la question des fondations logiques du calcul des probabilité). Bulletin de Statistique, 12, 13–21.

    Google Scholar 

  • Smets, P. (1988). Belief functions. In P. Smets, A. Mandani, P. Dubois, & H. Prade (Eds.), Non standard logics for automated reasoning (pp. 253–286). London: Academic.

    Google Scholar 

  • Smets, P. (1990). Constructing pignistic probability function in a context of uncertainty. In M. Henrion, R. D. Shachter, L. N. Kanal, & J. F. Lemmer (Eds.), Uncertainty in artificial intelligence 5 (pp. 29–40). Amsterdam: North Holland.

    Google Scholar 

  • Smets, P. (1991). Probability of provability and belief functions. Logique et Analyse, 133–134, 177–195.

    Google Scholar 

  • Smets, P. (1994). What is Dempster-Shafer’s model? In R. R. Yager, M. Fedrizzi, & J. Kacprzyk (Eds.), Advances in the Dempster-Shafer theory of evidence (pp. 5–34). New York/Chichester/Brisbane/Toronto: Wiley.

    Google Scholar 

  • Smets, P. (1997). The normative representation of quantified beliefs by belief functions. Artificial Intelligence, 92, 229–242.

    Google Scholar 

  • Smets, P. (1998). The transferable belief model for quantified belief representation. In P. Smets (Ed.), Handbook of defeasible reasoning and uncertainty management systems, Vol. 1: Quantified representation of uncertainty & imprecision (pp. 267–301). Dordrecht: Kluwer.

    Google Scholar 

  • Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2), 191–234.

    Google Scholar 

  • Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. London: W Strahan and T. Cadell.

    Google Scholar 

  • Smith, C. A. B. (1961). Consistency in statistical inference and decision (with discussion). Journal of the Royal Statistical Society, Series B, 23, 1–25.

    Google Scholar 

  • Smith, C. A. B. (1965). Personal probability and statistical analysis (with discussion). Journal of the Royal Statistical Society, Series A, 128, 469–499.

    Google Scholar 

  • Smith, H. L. (1990). Specification problems in experimental and nonexperimental social research. Sociological Methodology, 20, 59–91.

    Google Scholar 

  • Smith, H. L. (1997). Matching with multiple controls to estimate treatments effects in observational studies. In A. E. Raftery (Ed.), Sociological methodology 1997 (pp. 325–353). Oxford: Basil Blackwell.

    Google Scholar 

  • Smith, H. L. (2003). Some thoughts on causation as it relates to demography and population studies. Population and Development Review, 29(3), 459–469.

    Google Scholar 

  • Smith, H. L. (2009). Causation and its discontents. In H. Engelhardt, H.-P. Kohler, & A. Fürnkranz-Prskawetz (Eds.), Causal analysis in population studies (The Springer series on demographic methods and population analysis). Dordrecht/Heidelberg/London/New York: Springer.

    Google Scholar 

  • Smith, R. C., & Crosetti, A. H. (1954). Accuracy of ratio-correlation method for estimating postcensal population. Land Economics, 30(3), 279–280.

    Google Scholar 

  • Snow, P. (1998). On the correctness and reasonableness of Cox’s theorem for finite domains. Computational Intelligence, 14(3), 452–459.

    Google Scholar 

  • Sobel, M. E. (1995). Causal inference in the social and behavioural sciences. In M. E. Sobel (Ed.), Handbook of statistical modelling for the social and behavioural sciences, Arminger, Clogg (pp. 1–38). New York: Plenum.

    Google Scholar 

  • Solomonoff, R. J. (1960). A preliminary report on a general theory of inductive inference (Report ZTB-138). Cambridge, MA: Zator CO.

    Google Scholar 

  • Solomonoff, R. J. (1964a). A formal theory of inductive inference, Part 1. Information and Control, 7(1), 1–22.

    Google Scholar 

  • Solomonoff, R. J. (1964b). A formal theory of inductive inference, Part 2. Information and Control, 7(2), 224–254.

    Google Scholar 

  • Solomonoff, R. J. (1986). The applicability of algorithmic probability to problems in artificial intelligence. In L. N. Kanal & J. F. Lemmer (Eds.), Uncertainty in artificial intelligence (pp. 473–491). North-Holland: Elsevier Science Publishers B.V.

    Google Scholar 

  • Solomonoff, R. J. (1997). The discovery of algorithmic probability. Journal of Computer and System Science, 55(1), 73–88.

    Google Scholar 

  • Spearman, C. (1904). “General intelligence”, objectively determined and measured. The American Journal of Psychology, 15, 201–293.

    Google Scholar 

  • Starmer, C. (1992). Testing new theories of choice under uncertainty using the common consequence effect. Review of Economic Studies, 59(4), 813–830.

    Google Scholar 

  • Starmer, C. (2000). Developments in non expected-utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature, 38(2), 332–382.

    Google Scholar 

  • Steinhaus, H. (1923). Les probabilités dénombrables et leur rapport à la théorie de la mesure. Fundamenta Mathematicae, 4, 286–310.

    Google Scholar 

  • Stephan, F. F. (1942). An iterative method of adjusting sample frequency tables when expected marginal totals are known. Annals of Mathematical Statistics, 13(2), 166–178.

    Google Scholar 

  • Stigler, S. M. (1973). Studies on the history of probability and statistics. XXXII. Laplace, Fisher, and the discovery of the concept of sufficiency. Biometrika, 60(3), 439–445.

    Google Scholar 

  • Stigler, S. M. (1974). Studies on the history of probability and statistics. XXXIII. Cauchy and the witch of Agnesi: An historical note on the Cauchy distribution. Biometrika, 61(2), 375–380.

    Google Scholar 

  • Stigler, S. M. (1975). Studies on the history of probability and statistics. XXXIV. Napoleonic statistics: The work of Laplace. Biometrika, 62(2), 503–517.

    Google Scholar 

  • Stigler, S. M. (1982). Thomas Bayes Bayesian inference. Journal of the Royal Statistical Society, Series A, 145, 250–258.

    Google Scholar 

  • Stigler, S. M. (1986). The history of statistics: the measurement of uncertainty before 1900. Cambridge, MA: Belknap Press of Harvard University Press.

    Google Scholar 

  • Stigum, B. P. (1972). Finite state space and expected utility maximization. Econometrica, 40, 253–259.

    Google Scholar 

  • Stuart Mill, J. (1843). A system of logic, ratiocinative and inductive, being a connected view of the principles of evidence, and the methods of scientific investigation (2 vols). London: John W. Parker.

    Google Scholar 

  • Suppe, F. (1989). The semantic conception of theories and scientific realism. Urbana/Chicago: University of Illinois Press.

    Google Scholar 

  • Suppes, P. (1956). The role of subjective probability and utility in decision-making. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1954–1955(5), 61–73.

    Google Scholar 

  • Suppes, P. (1960). Some open problems in the foundations of subjective probability. In R. E. Machol (Ed.), Information and decision processes (pp. 129–143). New York: McGraw-Hill.

    Google Scholar 

  • Suppes, P. (1970). A probabilistic theory of causality. Amsterdam: North Holland.

    Google Scholar 

  • Suppes, P. (1974). The measurement of belief. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), 160–191.

    Google Scholar 

  • Suppes, P. (1976). Testing theories and the foundations of statistics. In W. L. Harper & C. A. Hooker (Eds.), Foundations of probability theory, statistical inference, and statistical theories of science, II (pp. 437–457). Dordrecht: Reidel.

    Google Scholar 

  • Suppes, P. (2002a). Representation and invariance of scientific structures. Stanford: CSLI Publications.

    Google Scholar 

  • Suppes, P. (2002b). Representation of probability. In P. Suppes (Ed.), Representation and invariance of scientific structures (pp. 129–264). Stanford: CSLI Publications.

    Google Scholar 

  • Suppes, P., & Zanotti, M. (1975). Necessary and sufficient conditions for existence of a unique measure strictly agreeing with a qualitative probability ordering. Journal of Philosophical Logic, 5, 431–438.

    Google Scholar 

  • Suppes, P., & Zanotti, M. (1982). Necessary and sufficient qualitative axioms for conditional probability. Zeitschrift für Wahrscheinlichkeitstheorie und Werwande Gebiete, 60, 163–169.

    Google Scholar 

  • Susarla, V., & van Rysin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71, 897–902.

    Google Scholar 

  • Susser, M. (1996). Choosing a future for epidemiology: I. Eras and paradigms. American Journal of Public Health, 86(5), 668–673.

    Google Scholar 

  • Süssmilch, J. P. (1741). Die göttliche Ordnung in den Veränderungen des menschlichen Geschlechts, aus der Geburt, Tod, und Fortpflanzung desselben erwiesen. Berlin: zu finden bei J. C. Spener.

    Google Scholar 

  • Süssmilch, J. P. (1761–1762). Die göttliche Ordnung in den Veränderungen des menschlichen Geschlechts, aus der Geburt, Tod, und Fortpflanzung desselben erwiesen. Berlin: Realschule.

    Google Scholar 

  • Sylla, E. D. (1998). The emergence of mathematical probability from the perspective of the Leibniz-Jacob Bernoulli correspondence. Perspectives on Science, 6(1&2), 41–76.

    Google Scholar 

  • Tabutin, D. (2007). Vers quelle(s) démographie(s)? Atouts, faiblesses et évolutions de la discipline depuis 50 ans. Population, 62(1), 15–32 (Wither demography? Strengths and weaknesses of the discipline over fifty years of change. Population-E, 62(1), 13–32).

    Google Scholar 

  • Thomas, D. H. (1986). Refiguring anthropology: First principles of probability & statistics. Long Grove: Waveland Press, Inc.

    Google Scholar 

  • Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286.

    Google Scholar 

  • Thurstone, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.

    Google Scholar 

  • Thurstone, L. L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.

    Google Scholar 

  • Tinbergen, J. (1939). Vérification statistique des théories de cycles économiques. Une méthode d’application au mouvement des investissements. Genève: SDN.

    Google Scholar 

  • Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1985). Statistical analysis of finite mixtures distributions. New York: Wiley.

    Google Scholar 

  • Todhunter, I. (1865). A history of the theory of probability from the time of Pascal to that of Laplace. Cambridge/London: Macmillan and Co.

    Google Scholar 

  • Torche, F. (2011). The effect of maternal stress on birth outcomes: Exploiting a natural experiment. Demography, 48(11), 1473–1491.

    Google Scholar 

  • Tornier, E. (1929). Wahrscheinlichkeisrechnunug und zalhlentheorie. Journal für die Teine und Angewandte Mathematik, 60, 177–198.

    Google Scholar 

  • Trussell, J. (1992). Introduction. In J. Trussell, R. Hankinson, & J. Tilton (Eds.), Demographic applications of event history analysis (pp. 1–7). Oxford: Clarendon Press.

    Google Scholar 

  • Trussell, J., & Richards, T. (1985). Correcting for unmeasured heterogeneity in hazard models using the Heckman-Singer procedure. In N. Tuma (Ed.), Sociological methodology (pp. 242–249). San-Francisco: Jossey-Bass.

    Google Scholar 

  • Trussell, J., & Rodriguez, G. (1990). Heterogeneity in demographic research. In J. Adams, D. A. Lam, A. I. Hermalin, & P. E. Smouse (Eds.), Convergent questions in genetics and demography (pp. 111–132). New York: Oxford University Press.

    Google Scholar 

  • Tuma, N. B., & Hannan, M. (1984). Social dynamics. London: Academic.

    Google Scholar 

  • Turing, A. M. (1936). On computable numbers, with an application to Endscheidungsproblem. Proceedings of the London Mathematical Society, 42(2), 230–265.

    Google Scholar 

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

    Google Scholar 

  • Tversky, A. (1974). Assessing uncertainty. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), 148–159.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(30), 453–457.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.

    Google Scholar 

  • Tversky, A., & Koeler, D. J. (1994). Support theory: A nonextensional representation of subjective probability. Psychological Review, 101, 547–567.

    Google Scholar 

  • Ulam, S. (1932). Zum Massbegriffe in Produkträumen. In Verhandlung des Internationalen Mathematiker-Kongress Zürich (Vol. II, pp. 118–119), Zurich: Orell Fiisli Verlag.

    Google Scholar 

  • Van Fraassen, B. (1980). The scientific image. Oxford: Clarendon Press.

    Google Scholar 

  • Van Horn, K. S. (2003). Constructing a logic of plausible inference: A guide to Cox’s theorem. International Journal of Approximate Reasoning, 34(1), 3–24.

    Google Scholar 

  • Van Imhoff, E., & Post, W. (1997). Méthodes de micro-simulation pour des projections de population, Population (D. Courgeau (Ed.)), 52(4), pp. 889–932 ((1998). Microsimulation methods for population projections. Population. An English Selection (D. Courgeau (Ed.)), 10(1), pp. 97–138).

    Google Scholar 

  • van Lambalgen, M. (1987). Von Mises’ definition of random sequences reconsidered. The Journal of Symbolic Logic, 32(3), 725–755.

    Google Scholar 

  • Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s ruses: Some surprising effects of selection on population dynamics. The American Statistician, 39, 176–185.

    Google Scholar 

  • Vaupel, J. W., Manton, K. G., & Stallard, E. (1979). The impact of heterogeneity in individual frailty data on the dynamics of mortality. Demography, 16(3), 439–454.

    Google Scholar 

  • Venn, J. (1866). The logic of chance. London: Macmillan.

    Google Scholar 

  • Vernon, P. E. (1950). The structure of human abilities. London: Methuen.

    Google Scholar 

  • Véron, J., & Rohrbasser, J.-M. (2000). Lodewijck et Christian Huygens: la distinction entre vie moyenne et vie probable. Mathématiques et sciences humaines, 149, 7–22.

    Google Scholar 

  • Véron, J., & Rohrbasser, J.-M. (2003). Wilhlem Lexis: la durée normale de la vie comme expression d’une « nature des choses ». Population, 58(3), 343–363.

    Google Scholar 

  • Vetta, A., & Courgeau, D. (2003). Demographic behaviour and behaviour genetics. Population-E, 58(4–5), 401–428 (French edition: (2003). Comportements démographiques et génétique du comportement, Population, 58(4–5), 457–488).

    Google Scholar 

  • Vidal, A. (1994). La pensée démographique. Doctrines, théories et politiques de population. Grenoble: Presses Universitaires de Grenoble.

    Google Scholar 

  • Vignaux, G. A., & Robertson, B. (1996). Lessons for the new evidence scholarship. In G. R. Heidbreder (Ed.), Maximum entropy and Bayesian methods, Proceedings of the 13th International Workshop, Santa Barbara, California, August 1–5, 1993 (pp. 391–401). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Ville, J. A. (1939). Étude critique de la notion de collectif. Paris: Gauthier-Villars.

    Google Scholar 

  • Vilquin, E. (1977). Introduction. In J. Graunt (Ed.), Observations naturelles et politiques (pp. 7–31). Paris: INED.

    Google Scholar 

  • Voltaire. (1734). Lettre XI. Sur l’insertion de la petite vérole. In Lettres Philosophiques, par M. de V… (pp. 92–149). Amsterdam: Chez E. Lucas, au Livre d’or.

    Google Scholar 

  • von Mises, R. (1919). Grundlagen der wahrscheinlichkeitesrechnung. Mathematische Zeitschrift, 5, 52–99.

    Google Scholar 

  • von Mises, R. (1928). Wahrscheinlichkeit, Statistik und Wahrheit. Wien: Springer (English translation: (1957). Probability, statistics and truth. London: George Allen & Unwin Ltd.).

    Google Scholar 

  • von Mises, R. (1932). Théorie des probabilités. Fondements et applications. Annales de l’Institut Henri Poincaré, 3(2), 137–190.

    Google Scholar 

  • von Mises, R. (1942). On the correct use of Bayes’s formula. Annals of Mathematical Statistics, 13, 156–165.

    Google Scholar 

  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behaviour. Princeton: Princeton University Press.

    Google Scholar 

  • Von Wright, G. H. (1971). Explanation and understanding. London: Routledge and Kegan Paul.

    Google Scholar 

  • Wachter, K., Blackwell, D., & Hammel, E. A. (1997). Testing the validity of kinship microsimulation. Journal of Mathematical and Computer Modeling, 26, 89–104.

    Google Scholar 

  • Wachter, K., Blackwell, D., & Hammel, E. A. (1998). Testing the validity of kinship microsimulation: An update. Website: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.2243, 36 p. Accessed August 5, 2005.

  • Waismann, F. (1930). Logische Analyse des wahrscheinlichkeisbegriffs. Erkenntnis, 1, 228–248.

    Google Scholar 

  • Wald, A. (1936). Sur la notion de collectif dans le calcul des probabilités. Comptes Rendus des Séances de l’Académie des Sciences, 202, 180–183.

    Google Scholar 

  • Wald, A. (1947a). Foundations of a general theory of sequential decision functions. Econometrica, 15(4), 279–313.

    Google Scholar 

  • Wald, A. (1947b). Sequential analysis. New York: Wiley.

    Google Scholar 

  • Wald, A. (1949). Statistical decision functions. Annals of Mathematical Statistics, 20(2), 165–205.

    Google Scholar 

  • Wald, A. (1950). Statistical decision functions. New York: Wiley.

    Google Scholar 

  • Walliser, B. (Ed.). (2009). La cumulativité du savoir en sciences sociales. Lassay-les-Châteaux: Éditions de l’École des Hautes Études en Sciences Sociales.

    Google Scholar 

  • Wargentin, P. W. (1766). Mortaliteten i Sverige, i anledning af Tabell-Verket. Kongl. Svenska Vetenskap Academiens Handlingar, XXVII, 1–25.

    Google Scholar 

  • Wavre, R. (1938–1939). Colloque consacré à la théorie des probabilités, Fascicules 734–740; 766. Paris: Hermann.

    Google Scholar 

  • Weber, M. (1998). The resilience of the Allais paradox. Ethics, 109(1), 94–118.

    Google Scholar 

  • Weber, B. (2007). The effects of losses and event splitting on the Allais paradox. Judgment and Decision Making, 2, 115–125.

    Google Scholar 

  • Whelpton, P. (1946). Reproduction rates adjusted for age, parity, fecundity and marriage. Journal of the American Statistical Association, 41, 501–516.

    Google Scholar 

  • Whelpton, P. (1949). Cohort analysis of fertility. American Sociological Review, 14(6), 735–749.

    Google Scholar 

  • West, M., Mueller, P., & Escobar, M.D. (1994). Hierarchical priors and mixture models, with applications in regression and density estimation. In P.R. Freeman and A.F.M. Smith, Aspects of uncertainty: A tribute to D.V. Lindley (pp. 363–386), London: Wiley Series in Probability and Statistics.

    Google Scholar 

  • Wilks, S. S. (1941). Book review of Jeffreys’ Theory of probability. Biometrika, 32, 192–194.

    Google Scholar 

  • Williamson, J. (2005). Bayesian nets and causality: Philosophical and computational foundations. Oxford: Oxford University Press.

    Google Scholar 

  • Williamson, J. (2009). Philosophies of probability. In A. Irvine (Ed.), Handbook of the philosophy of mathematics (Handbook of the philosophy of science, Vol. 4, pp. 1–40). Amsterdam: Elsevier/North-Holland.

    Google Scholar 

  • Wilson, M. C. (2007). Uncertainty and probability in institutional economics. Journal of Economic Issues, 41(4), 1087–1108.

    Google Scholar 

  • Wolfe, J. H. (1965). A computer program for the maximum-likelihood analysis of types (Technical Bulletin 65–15). U. S. Naval Personnel Research Activity, San Diego (Defense Documentation Center AD 620 026).

    Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

    Google Scholar 

  • Wrinch, D., & Jeffreys, H. (1919). On some aspects of the theory of probability. Philosophical Magazine, 38, 715–731.

    Google Scholar 

  • Wrinch, D., & Jeffreys, H. (1921). On certain fundamental principles of scientific inquiry. Philosophical Magazine, 42, 369–390.

    Google Scholar 

  • Wrinch, D., & Jeffreys, H. (1923). On certain fundamental principles of scientific inquiry. Philosophical Magazine, 45, 368–374.

    Google Scholar 

  • Wunsch, G. (1994). L’analyse causale en démographie. In R. Franck (Ed.), Faut-il chercher aux causes une raison ? L’explication causale dans les sciences humaines (pp. 24–40). Paris: Librairie Philosophique J. Vrin.

    Google Scholar 

  • Yager, R. R., & Liu, L. (Eds.). (2007). Classic works on the Dempster-Shafer theory of belief functions. Heidelberg: Springer.

    Google Scholar 

  • Yashin, A. I., & Manton, K. G. (1997). Effects of unobserved and partially observed covariate process on system failure: A review of models and estimation strategies. Statistical Science, 12, 20–34.

    Google Scholar 

  • Younes, H., Delampady, M., MacGibbon, B., & Cherkaoui, O. (2007). A hierarchical Bayesian approach to the estimation of monotone hazard rates in the random right censoring model. Journal of Statistical Research, 41(2), 35–62.

    Google Scholar 

  • Yule, U. (1895). On the correlation of total pauperism with proportion of out-relief, I: All ages. The Economic Journal, 5, 603–611.

    Google Scholar 

  • Yule, U. (1897). On the theory of correlation. Journal of the Royal Statistical Society, 60, 812–854.

    Google Scholar 

  • Yule, U. (1899). An investigation into the causes of changes in pauperism in England, chiefly during the last two intercensal decades, I. Journal of the Royal Statistical Society, 62, 249–295.

    Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

    Google Scholar 

  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Courgeau, D. (2012). Conclusion to Part II. In: Probability and Social Science. Methodos Series, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2879-0_7

Download citation

Publish with us

Policies and ethics