Advertisement

Conclusion to Part II

  • Daniel Courgeau
Chapter
Part of the Methodos Series book series (METH, volume 10)

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?

Keywords

Event History Multilevel Analysis Cohort Analysis Event History Analysis Newtonian Physic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aalen, O. O. (1975). Statistical inference for a family of counting processes. PhD thesis, University of California, Berkeley.Google Scholar
  2. Aalen, O. O. (1978). Nonparametric inference for a family of counting processes. The Annals of Statistics, 6(4), 701–726.Google Scholar
  3. 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
  4. 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
  5. Aalen, O. O., Borgan, Ø., & Gjessing, H. K. (2008). Survival and event history analysis. New York: Springer.Google Scholar
  6. 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
  7. 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
  8. Aczèl, J., Forte, B., & Ng, C. T. (1974). Why the Shannon and Hartley entropies are ‘natural’. Advanced Applied Probabilities, 6, 131–146.Google Scholar
  9. Agazzi, E. (1985). Commensurability, incommensurability and cumulativity in scientific knowledge. Erkenntnis, 22(1–3), 51–77.Google Scholar
  10. Agliardi, E. (2004). Axiomatization and economic theories: Some remarks. Revue Economique, 55(1), 123–129.Google Scholar
  11. 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
  12. 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
  13. Andersen, P. K., Borgan, Ø., Gill, R. D., & Keiding, N. (1993). Statistical models based on counting processes. New York/Berlin/Heidelberg: Springer.Google Scholar
  14. 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
  15. 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.
  16. 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.
  17. Aristotle. (around 330 B.C.). Politics. The Internet Classic Archive (B. Jowett, Trans.). http://classics.mit.edu/Aristotle/politics.html. Accessed August 30, 2011.
  18. 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.
  19. 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.
  20. Armatte, M. (2004). L’axiomatisation et les théories économiques: un commentaire. Revue Economique, 55(1), 130–142.Google Scholar
  21. 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
  22. Arnauld, A., & Nicole, P. (1662). La logique ou l’Art de penser. Paris: Chez Charles Savreux.Google Scholar
  23. Arnborg, S. (2006). Robust Bayesianism: Relation to evidence theory. Journal of Advances in Information Fusion, 1(1), 75–90.Google Scholar
  24. Arnborg, S., & Sjödin, G. (2000). Bayes rules in finite models. Proceedings of the European Conference on Artificial Intelligence, Berlin, pp. 571–575.Google Scholar
  25. 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
  26. 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
  27. Bacon, F. (1605). The two books of Francis Bacon, of the proficience and advancement of learning, divine and humane. London: Henrie Tomes.Google Scholar
  28. Bacon, F. (1620). Novum Organum. London: J. Bill.Google Scholar
  29. Bacon, F. (1623). Historia vitae et mortis. Londini: In Officio Io. Haviland, impensis Matthei Lownes.Google Scholar
  30. Barbin, E., & Lamarche, J. P. (Eds.). (2004). Histoires de probabilités et de statistiques. Paris: Ellipses.Google Scholar
  31. Barbin, E., & Marec, Y. (1987). Les recherches sur la probabilité des jugements de Simon-Denis Poisson. Histoire & Mesure, 11(2), 39–58.Google Scholar
  32. Barbut, M. (1968). Les treillis des partitions d’un ensemble fini et leur représentation géométrique. Mathématiques et Sciences Humaines, 22, 5–22.Google Scholar
  33. 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
  34. Barbut, M., & Monjardet, B. (1970). Ordre et classification. Algèbre et combinatoire. Tomes 1, 2. Paris: Librairie Hachette.Google Scholar
  35. Bartholomew, D. J. (1975). Probability and social science. International Social Science Journal, XXVII(3), 421–436.Google Scholar
  36. Bateman, B. W. (1987). Keynes changing conception of probability. Economics and Philosophy, 3(1), 97–120.Google Scholar
  37. 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
  38. Bechtel, W., & Richardson, R. C. (1993). Discovering complexity. Princeton: Princeton University Press.Google Scholar
  39. 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.
  40. Bentham, J. (1823). Traité des preuves judiciaires. Paris: Etienne Dumont.Google Scholar
  41. Benzécri, J. P., et collaborateurs. (1973). L’analyse des données (2 vols). Paris: Dunod.Google Scholar
  42. 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
  43. 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
  44. 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
  45. Bernardo, J. M., & Smith, A. F. M. (1994). Bayesian theory. Chichester: Wiley.Google Scholar
  46. Bernoulli, N. (1709). Dissertatio inauguralis mathematico-juridica, de usu artis conjectandi in jure. Bâle.Google Scholar
  47. Bernoulli, J. I. (1713). Ars conjectandi. Bâle: Impensis Thurnisiorum fratrum.Google Scholar
  48. Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Commentarii Academiae Scientiarum Imperialis Petropolitanae, V, 175–192.Google Scholar
  49. 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
  50. 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
  51. 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
  52. Bernstein, F. (1917). Oпыт aкcиoмaтичecкoгo oбocнoвaния тeopии вepoятнocтeй. Communication de la Société Mathématique de Karkov, 15, 209–274.Google Scholar
  53. Bertalanffy, L. (1968). General system theory. Foundations, development, applications. New York: John Braziler.Google Scholar
  54. Berthelot, J.-M. (Ed.). (2001). Épistémologie des sciences sociales. Paris: Presses Universitaires de France.Google Scholar
  55. Bertrand, J. (1889). Calcul des probabilités. Paris: Gauthier-Villard et Fils.Google Scholar
  56. 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
  57. 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
  58. 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
  59. 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
  60. Bijak, J. (2011). Forecasting international migration (Springer series on demographic methods and population analysis, Vol. 24). Dordrecht/Heidelberg/London/New York: Springer.Google Scholar
  61. 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
  62. Birkhoff, G. (1935). Abstract-linear dependence and lattices. American Journal of Mathematics, 57(4), 800–804.Google Scholar
  63. Blayo, C. (1995). La condition d’homogénéité en analyse démographique et en analyse statistique des biographies. Population, 50(6), 1501–1518.Google Scholar
  64. 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
  65. 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
  66. Bocquet-Appel, J.-P., & Masset, C. (1982). Farewell to paleodemography. Journal of Human Evolution, 11, 321–333.Google Scholar
  67. Bocquet-Appel, J.-P., & Masset, C. (1996). Paleodemography: Expectancy and false hope. American Journal of Physical Anthropology, 99, 571–583.Google Scholar
  68. Bod, R., Hay, J., & Jannedy, S. (Eds.). (2003). Probabilistic linguistic. Cambridge, MA: MIT Press.Google Scholar
  69. 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
  70. Boltzmann, L. (1871). Einige allgemeine Sätze über wärmegleichgewicht. Sitzungberichte, K. Akademie der Wissenshaften, Wien, Mathematisch-Naturwissenchaftlichte Klasse, 63, 679–711.Google Scholar
  71. 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
  72. 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
  73. 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
  74. Bordas-Desmoulins, J.-B. (1843). Le cartésianisme ou la véritable rénovation des sciences. Paris: J. Hetzel.Google Scholar
  75. Borel, E. (1898). Leçons sur la théorie des fonctions. Paris: Gauthier-Villars et fils.Google Scholar
  76. Borel, E. (1909). Eléments de la théorie des probabilités. Paris: Librairie Hermann.Google Scholar
  77. Borel, E. (1914). Le hasard. Paris: Librairie Félix Alcan.Google Scholar
  78. Borsboom, D., Mellenberg, G. J., & van Herden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.Google Scholar
  79. 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
  80. Braithwaite, R. B. (1941). Book review of Jeffreys’ Theory of probability. Mind, 50, 198–201.Google Scholar
  81. Bremaud, P. (1973). A martingale approach to point processes. Memorandum ERL-M345, Electronic research laboratory. Berkeley: University of California.Google Scholar
  82. 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
  83. 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
  84. 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
  85. Brian, E., & Jaisson, M. (2007). The descent of human sex ratio at birth (Methodos series, Vol. 4). Dordrecht: Springer.Google Scholar
  86. Broggi, U. (1907). Die Axiome der Wahrscheinlichkeitsrechnung. PhD thesis, Universität Göttingen, Göttingen.Google Scholar
  87. 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.
  88. Buck, C. E., Cavanagh, W. G., & Litton, W. G. (1996). Bayesian approach to interpreting archaeological data. Chichester: Wiley.Google Scholar
  89. 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
  90. 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
  91. 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
  92. Cantelli, F. P. (1932). Una teoria astratta del calcolo delle probabilità. Giornale dell’Istituto Italiano degli Attuari, 8, 257–265.Google Scholar
  93. 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
  94. Cantillon, R. (1755). Essai sur la nature du commerce en général. London: Fletcher Gyles.Google Scholar
  95. 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
  96. Cantor, G. (1874). Über eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen. Journal de Crelle, 77, 258–262.Google Scholar
  97. Cardano, J. (1663). Liber De Ludo Aleae. In Opera Omnia,Tomus I, Lugduni.Google Scholar
  98. 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
  99. Carnap, R. (1933). L’ancienne et la nouvelle logique. Paris: Hermann.Google Scholar
  100. Carnap, R. (1950). The logical foundations of probability. Chicago: University of Chicago Press.Google Scholar
  101. Carnap, R. (1952). The continuum of inductive methods. Chicago: University of Chicago Press.Google Scholar
  102. Carnot, S. (1824). Réflexions sur la puissance motrice du feu et les machines propres à développer cette puissance. Paris: Bachelier.Google Scholar
  103. 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
  104. Cartwright, N. (2007). Hunting causes and using them: Approaches in philosophy and economics. Cambridge, MA: Cambridge University Press.Google Scholar
  105. 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
  106. 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
  107. 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
  108. 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
  109. 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
  110. Charbit, Y. (2010). The classical foundations of population thought from Plato to Marx. Heidelberg/London: Springer.Google Scholar
  111. 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
  112. Choquet, G. (1953). Theory of capacities. Annales de l’Institut Fourier, 5, 131–296.Google Scholar
  113. Chung, K.-L. (1942). On mutually favourable events. The Annals of Statistics, 13, 338–349.Google Scholar
  114. Church, A. (1932). An unsolvable problem of elementary number theory. American Journal of Mathematics, 58(2), 345–363.Google Scholar
  115. Church, A. (1940). On the concept of a random sequence. Bulletin of the American Mathematical Society, 46(2), 130–135.Google Scholar
  116. 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
  117. Clayton, D. (1991). A Monte-Carlo method for Bayesian inference in frailty models. Biometrics, 47, 467–485.Google Scholar
  118. Cohen, M. R., & Nagel, E. (1934). An introduction to logic and scientific method. New York: Harcourt Brace.Google Scholar
  119. 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
  120. Colyvan, M. (2004). The philosophical significance of Cox’s theorem. International Journal of Approximate Reasoning, 37(1), 71–85.Google Scholar
  121. Colyvan, M. (2008). Is probability the only coherent approach to uncertainty? Risk Analysis, 28(3), 645–652.Google Scholar
  122. Comte, A. (1830–1842). Cours de philosophie positive. Paris: Bachelier.Google Scholar
  123. Condorcet. (1778). Sur les probabilités. Histoire de l’Académie Royale de Sciences, 1781, 43–46.Google Scholar
  124. 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
  125. 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
  126. 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
  127. Copeland, A. (1928). Admissible numbers in the theory of probability. American Journal of Mathematics, 50, 535–552.Google Scholar
  128. 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
  129. Copernic, N. (1543). De revolutionibus orbium coelestum. Nuremberg: Johanes Petreius ed.Google Scholar
  130. Coumet, E. (1970). La théorie du hasard est-elle née par hasard? Annales: Economies, Sociétés Civilisations, 25(3), 574–598.Google Scholar
  131. 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
  132. Council of the Statistical Society of London. (1838). Introduction. Journal of the Statistical Society of London, 1(1), 1–5.Google Scholar
  133. 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
  134. Courgeau, D. (1985). Interaction between spatial mobility, family and career life-cycle: A French survey. European Sociological Review, 1(2), 139–162.Google Scholar
  135. Courgeau, D. (1991). Analyse de données biographiques erronées. Population, 46(1), 89–104.Google Scholar
  136. Courgeau, D. (1992). Impact of response error on event history analysis. Population: An English Selection, 4, 97–110.Google Scholar
  137. Courgeau, D. (2002). Evolution ou révolutions dans la pensée démographique? Mathématiques et Sciences Humaines, 40(160), 49–76.Google Scholar
  138. 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
  139. Courgeau, D. (2004a). Du groupe à l’individu. Synthèse multiniveau. Paris: INED.Google Scholar
  140. Courgeau, D. (2004b). Probabilités, démographie et sciences sociales. Mathématiques et Sciences Humaines, 42(3), 5–19.Google Scholar
  141. Courgeau, D. (2007a). Multilevel synthesis. From the group to the individual. Dordrecht: Springer.Google Scholar
  142. Courgeau, D. (2007b). Inférence statistique, échangeabilité et approche multiniveau. Mathématiques et Sciences Humaines, 45(179), 5–19.Google Scholar
  143. 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
  144. 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
  145. Courgeau, D. (2011). Critiques des méthodes actuellement utilisés. In I. Séguy & L. Buchet (Eds.), Manuel de paléodémographie (pp. 255–290). Paris: INED.Google Scholar
  146. 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
  147. Courgeau, D., & Lelièvre, E. (1986). Nuptialité et agriculture. Population, 41(2), 303–326.Google Scholar
  148. 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
  149. 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
  150. 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
  151. Cournot, A.-A. (1843). Exposition de la théorie des chances et des probabilités. Paris: Hachette.Google Scholar
  152. Cox, R. (1946). Probability, frequency, and reasonable expectation. American Journal of Physics, 14, 1–13.Google Scholar
  153. Cox, R. (1961). The algebra of probable inference. Baltimore: The John Hopkins Press.Google Scholar
  154. Cox, D. R. (1972). Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, 34(2), 187–220.Google Scholar
  155. Cox, D. R. (1975). Partial likelihood. Biometrika, 62, 269–276.Google Scholar
  156. 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
  157. Cox, D. R. (2006). Principles of statistical inference. Cambridge, UK: Cambridge University Press.Google Scholar
  158. Cox, D. R., & Hinkley, D. V. (1974). Theoretical statistics. London: Chapman & Hall.Google Scholar
  159. Cox, D. R., & Oakes, D. (1984). Analysis of survival data. London/New York: Chapman & Hall.Google Scholar
  160. Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neurosciences. Oxford: Clarendon Press.Google Scholar
  161. Cribari-Neto, F., & Zarkos, S. G. (1999). Yet another econometric programming environment. Journal of Applied Econometrics, 14, 319–329.Google Scholar
  162. 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
  163. 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
  164. 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
  165. 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
  166. Darden, L. (2002). Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward/backward chaining. Philosophy of Science (Supplement), 69, S354–S365.Google Scholar
  167. Darden, L. (Ed.). (2006). Reasoning in biological discoveries. Cambridge, MA: Cambridge University Press.Google Scholar
  168. Daston, L. (1988). Classical probabilities in the enlightenment. Princeton: Princeton University.Google Scholar
  169. Daston, L. (1989). L’interprétation classique du calcul des probabilités. Annales: Economies, Sociétés, Civilisations, 44(3), 715–731.Google Scholar
  170. 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
  171. 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
  172. Dawid, A. P. (2000). Causal inference without counterfactuals. Journal of the American Statistical Association, 95(450), 407–448.Google Scholar
  173. 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
  174. Dawid, A. P., & Mortera, J. (1996). Coherent analysis of forensic identification evidence. Journal of the Royal Statistical Society, 58(2), 425–453.Google Scholar
  175. de Fermat, P. (1679). Varia opera mathemetica Petri de Fermat, Senatoris Tolosianis. Toulouse: Joannen Pech.Google Scholar
  176. de Finetti, B. (1931a). Sul significato soggettivo della probabilità. Fundamenta Mathematicae, 17, 298–329.Google Scholar
  177. de Finetti, B. (1931b). Sul concetto di media. Giornale dell’Instituto Italiano degli Attuari, 2, 367–396.Google Scholar
  178. de Finetti, B. (1937). La prévision: ses lois logiques, ses sources subjectives. Annales de l’Institut Henri Poincaré, 7(Paris), 1–68.Google Scholar
  179. 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
  180. de Finetti, B. (1952). Sulla preferibilità. Giornale degli Economisti e Annali de Economia, 11, 685–709.Google Scholar
  181. 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
  182. de Finetti, B. (1974). Theory of probability (2 vols). London/New York: Wiley.Google Scholar
  183. de Finetti, B. (1985). Cambridge probability theorists. The Manchester School, 53, 348–363.Google Scholar
  184. 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
  185. 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
  186. de Montessus, R. (1908). Leçons élémentaires sur le calcul des probabilités. Paris: Gauthier Villars.Google Scholar
  187. de Montmort, P. R. (1713). Essay d’analyse sur les jeux de hazard (2nd ed.). Paris: Jacques Quillau.Google Scholar
  188. Degenne, A., & Forsé, M. (1994). Les réseaux sociaux. Une approche structurale en sociologie. Paris: Armand Colin.Google Scholar
  189. Degenne, A., & Forsé, M. (1999). Introducing social networks. London: Sage.Google Scholar
  190. DeGroot, M. H. (1970). Optimal statistical decision. New York: McGraw-Hill.Google Scholar
  191. Delannoy, M. (1895). Sur une question de probabilités traitée par d’Alembert. Bulletin de la S.M.F., Tome 23, 262–265.Google Scholar
  192. Delaporte, P. (1941). Evolution de la mortalité en Europe depuis les origines des statistiques de l’état civil. Paris: Imprimerie Nationale.Google Scholar
  193. 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
  194. 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
  195. Dempster, A. P. (1967). Upper and lower probabilities induced by a multilevel mapping. Annals of Mathematical Statistics, 38, 325–339.Google Scholar
  196. Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, Series B, 30, 205–245.Google Scholar
  197. Deparcieux, A. (1746). Essai sur les probabilités de la durée de la vie humaine. Paris: Frères Guerin.Google Scholar
  198. Descartes, R. (1647). Méditations, objections et réponses. In Œuvres et lettres. Paris: Gallimard.Google Scholar
  199. Desrosières, A. (1993). La politique des grands nombres: histoire de la raison statistique. Paris: La Découverte.Google Scholar
  200. Destutt de Tracy, A. L. C. (1801). Elémens d’idéologie (Vol. 4). Paris: Pierre Firmin Didot, An IX (Seconde édition (1804–1818)).Google Scholar
  201. Doob, J. L. (1940). Regularity properties of certain families of chance variables. Transactions of the American Mathematical Society, 44(1), 455–486.Google Scholar
  202. 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
  203. Doob, J. L. (1953). Stochastic processes. New York: Wiley.Google Scholar
  204. Dormoy, E. (1874). Théorie mathématique des assurances sur la vie. Journal des Actuaires Français, 3, 283–299, 432–461.Google Scholar
  205. 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
  206. 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
  207. 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
  208. 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
  209. Dupâquier, J. (1996). L’invention de la table de mortalité. Paris: Presses Universitaires de France.Google Scholar
  210. Durkheim, E. (1895). Les règles de la méthode sociologique. Paris: Alcan.Google Scholar
  211. Durkheim, E. (1897). Le suicide. Paris: Alcan.Google Scholar
  212. 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
  213. Edgeworth, F. Y. (1883). The method of least squares. Philosophical Magazine, 5th Series, 16, 360–375.Google Scholar
  214. 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
  215. 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
  216. Edgeworth, F. Y. (1892). Correlated averages. Philosophical Magazine, 5th Series, 34, 190–204.Google Scholar
  217. Edgeworth, F. Y. (1893a). Note on the correlation between organs. Philosophical Magazine, 5th Series, 36, 350–351.Google Scholar
  218. Edgeworth, F. Y. (1893b). Statistical correlations between social phenomena. Journal of the Royal Statistical Society, 56, 852–853.Google Scholar
  219. Edgeworth, F. Y. (1895). On some recent contributions to the theory of statistics. Journal of the Royal Statistical Society, 58, 506–515.Google Scholar
  220. Einstein, A. (1905). Zur Elektrodynamik bewegter Körper. Annalen der Physik, 332(10), 891–921.Google Scholar
  221. Ellis, R. L. (1849). On the Foundations of the theory of probability. Transactions of the Cambridge Philosophical Society, VIII, 1–16.Google Scholar
  222. Ellsberg, D. (1961). Risk, ambiguity and the Savage axioms. Quarterly Journal of Economics, 75(4), 643–669.Google Scholar
  223. Ellsberg, D. (2001). Risk, ambiguity and decision. New York/London: Garland Publishing Inc.Google Scholar
  224. Eriksson, L., & Hájek, A. (2007). What are degrees of belief? Studia Logica, 86, 185–215.Google Scholar
  225. 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
  226. Feller, W. (1934). Review of Kolmogorov (1933). Zentralblatt für Mathematik und ihre Grenzegebiete, 7, 216.Google Scholar
  227. Feller, W. (1950). An introduction to the theory of probability and its applications (Vol. 1). New York: Wiley.Google Scholar
  228. Feller, W. (1961). An introduction to the theory of probability and its applications (Vol. 2). New York: Wiley.Google Scholar
  229. Fergusson, T. S. (1973). A Bayesian analysis of some parametric problems. The Annals of Statistics, 1, 209–230.Google Scholar
  230. Fishburn, P. C. (1964). Decision and value theory. New York: Wiley.Google Scholar
  231. Fishburn, P. C. (1975). A theory of subjective probability and expected utilities. Theory and Decision, 6, 287–310.Google Scholar
  232. Fishburn, P. C. (1986). The axioms of subjective probability. Statistical Science, 1(3), 335–345.Google Scholar
  233. Fisher, R. A. (1922a). The mathematical theory of probability. London: Macmillan.Google Scholar
  234. Fisher, R. A. (1922b). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society, Series A, 222, 309–368.Google Scholar
  235. Fisher, R. A. (1923). Statistical tests of agreement between observation and hypothesis. Economica, 3, 139–147.Google Scholar
  236. Fisher, R. A. (1925a). Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society, 22, 700–725.Google Scholar
  237. Fisher, R. A. (1925b). Statistical methods for research workers. Edinburgh: Olivier and Boyd.Google Scholar
  238. 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
  239. 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
  240. Fisher, R. A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society, 98, 39–82.Google Scholar
  241. Fisher, R. A. (1956). Statistical methods and scientific inference. Edinburgh: Oliver and Boyd.Google Scholar
  242. Fisher, R. A. (1958). The nature of probability. Centennial Review, 2, 261–274.Google Scholar
  243. 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
  244. 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
  245. 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
  246. Franck, R. (Ed.). (1994). Faut-il chercher aux causes une raison? L’explication causale dans les sciences humaines. Paris: Librairie Philosophique Vrin.Google Scholar
  247. Franck, R. (1995). Mosaïques, machines, organismes et sociétés. Examen métadisciplinaire du réductionnisme. Revue Philosophique de Louvain, 93, 67–81.Google Scholar
  248. 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
  249. 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
  250. 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
  251. Franklin, J. (2001). Resurrecting logical probability. Erkenntnis, 55, 277–305.Google Scholar
  252. 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
  253. Fréchet, M. (1937). Généralités sur le calcul des probabilités. Variables aléatoires. Paris: Gauthier Villars.Google Scholar
  254. 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
  255. 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
  256. Freund, J. E. (1965). Puzzle or paradox? The American Statistician, 19(4), 29–44.Google Scholar
  257. 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
  258. Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. The Journal of Political Economy, LVI(4), 279–304.Google Scholar
  259. 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
  260. Gacôgne, L. (1993). About a foundation of the Dempster’s rule, Rapport 93/27 Laforia.Google Scholar
  261. 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
  262. Galileo, G. (1613). Istoria e dimostrazioni intorno alle macchie solari e loro accidenti. Roma: Giacomo Mascardi.Google Scholar
  263. Galileo, G. (1898). Sopra le scoperte de i dadi. In Opere (Vol. VIII, pp. 591–594). Firenze: Barbera Editore.Google Scholar
  264. Galton, F. (1875). Statistics by intercomparison, with remarks on the law of frequency of error. Philosophical Magazine, 4th Series, 49, 33–46.Google Scholar
  265. 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
  266. Galton, F. (1886b). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15, 246–263.Google Scholar
  267. Galton, F. (1888). Co-relations and their measurement, chiefly from anthropometric data. Proceedings of the Royal Society of London, 45, 135–145.Google Scholar
  268. Gärdenfors, P., Hansson, B., & Sahlin, N.-E. (Eds.). (1983). Evidential value: Philosophical, judicial and psychological aspects of a theory. Lund: Gleerups.Google Scholar
  269. 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
  270. 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
  271. Gauss, C. F. (1809). Theoria motus corporum celestium. Hamburg: Perthes et Besser.Google Scholar
  272. Gauss, C. F. (1816). Bestimmung der Genauigkeit der boebechtungen. Zeitshrifte für Astronomie und Verwandte Wissenschaften, 1, 185–216.Google Scholar
  273. Gauss, C. F. (1823). Theoria combinationis observationum erroribus minimis obnoxiae. Göttingen: Dieterich.Google Scholar
  274. 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.
  275. 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
  276. Gelman, A., Karlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. New York: Chapman & Hall.Google Scholar
  277. 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
  278. 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
  279. 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
  280. Gibbs, J. W. (1902). Elementary principles in statistical mechanics. New Haven: Yale University Press.Google Scholar
  281. 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
  282. 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
  283. Gill, J. (2008). Bayesian methods. A social and behavioral sciences approach. Boca Raton: Chapman & Hall.Google Scholar
  284. Gillies, D. (2000). Philosophical theories of probability. London/New York: Routledge.Google Scholar
  285. 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
  286. 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
  287. 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
  288. Glennan, S. (2002). Rethinking mechanical explanations. Philosophy of Science, 69(Proceedings), S342–S353.Google Scholar
  289. 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
  290. Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. Monatshefte für Mathematik und Physik, 38, 173–198.Google Scholar
  291. Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least-squares. Biometrika, 73, 43–56.Google Scholar
  292. Goldstein, H. (1987). Multilevel covariance component models. Biometrika, 74, 430–431.Google Scholar
  293. Goldstein, H. (1991). Nonlinear multilevel models, with an application to discrete response data. Biometrika, 78, 45–51.Google Scholar
  294. Goldstein, H. (2003). Multilevel statistical models. London: Edward Arnold.Google Scholar
  295. Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality. Philosophical Transactions, 115, 513.Google Scholar
  296. Gonseth, F. (1975). Le référentiel, univers obligé de médiatisation. Lausanne: Editions l’Age d’Homme.Google Scholar
  297. Good, I. J. (1952). Rational decisions. Journal of the Royal Statistical Society, Series B, 14, 107–114.Google Scholar
  298. Good, I. J. (1956). Which comes first, probability or statistics? Journal of the Institute of Actuaries, 42, 249–255.Google Scholar
  299. 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
  300. Good, I. J. (1971). 46656 varieties of Bayesians. The American Statistician, 25, 62–63.Google Scholar
  301. 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
  302. Good, I. J. (1983). Good thinking. Minneapolis: University of Minnesota Press.Google Scholar
  303. Gosset, W. S. (Student) (1908a). The probable error of a mean. Biometrika, 6(1), 1–25.Google Scholar
  304. Gosset, W. S. (Student) (1908b). Probable error of a correlation coefficient. Biometrika, 6(2–3), 302–310.Google Scholar
  305. Gould, S. J. (1981). The mismeasure of man. New York: W.W. Norton & Co.Google Scholar
  306. Gouraud, C. (1848). Histoire du calcul des probabilités depuis ses origines jusqu’à nos jours. Paris: Auguste Durand.Google Scholar
  307. Graetzer, J. (1883). Edmund Halley und Caspar Neumann: Ein Beitrag zur Geschichte der Bevölkerungsstatistik. Breslau: Schottlaender.Google Scholar
  308. Granger, G.-G. (1967). Épistémologie économique. In J. Piaget (Ed.), Logique et connaissance scientifique (pp. 1019–1055). Paris: Editions Gallimard.Google Scholar
  309. Granger, G.-G. (1976). La théorie aristotélicienne de la science. Paris: Éditions Aubier Montaigne.Google Scholar
  310. Granger, G.-G. (1988). Essai d’une philosophie du style. Paris: Editions Odile Jacob.Google Scholar
  311. Granger, G.-G. (1992). A quoi sert l’épistémologie? Droit et Société, 20/21, 35–42.Google Scholar
  312. Granger, G.-G. (1994). Formes, opérations, objets. Paris: Librairie Philosophique Vrin.Google Scholar
  313. 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
  314. Greenland, S. (1998a). Probability logic and probabilistic induction. Epidemiology, 9, 322–332.Google Scholar
  315. Greenland, S. (1998b). Induction versus Popper: Substance versus semantics. International Journal of Epidemiology, 27, 543–548.Google Scholar
  316. Greenland, S. (2000). Principles of multilevel modelling. International Journal of Epidemiology, 29, 158–167.Google Scholar
  317. 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
  318. 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
  319. Guillard, A. (1855). Eléments de statistique humaine ou démographie comparée. Paris: Guillaumin.Google Scholar
  320. Gurr, T. R. (1993). Minorities at risk: A global view of ethnopolitical conflicts. Washington, DC: United States Institute of Peace Progress.Google Scholar
  321. Gustafson, P. (1998). Flexible Bayesian modelling for survival data. Lifetime Data Analysis, 4, 281–299.Google Scholar
  322. Gustafsson, J.-E. (1984). A unifying model of the structure of intellectual abilities. Intelligence, 8, 179–203.Google Scholar
  323. Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11, 1–12.Google Scholar
  324. Hacking, I. (1965). Logic of statistical inference. Cambridge, UK: Cambridge University Press.Google Scholar
  325. Hacking, I. (1975). The emergence of probability. Cambridge, UK: Cambridge University Press.Google Scholar
  326. Hacking, I. (1990). The taming of science. Cambridge, UK: Cambridge University Press.Google Scholar
  327. Hacking, I. (2001). An introduction to probability and inductive logic. Cambridge, UK: Cambridge University Press.Google Scholar
  328. Hadamard, J. (1922). Les principes du calcul des probabilités. Revue de Métaphysique et de Morale, 29(3), 289–293.Google Scholar
  329. 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.
  330. 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.
  331. Hald, A. (1990). A history of probability and statistics and their applications before 1750. New York: Wiley.Google Scholar
  332. Hald, A. (1998). A history of mathematical statistics from 1750 to 1930. New York: Wiley.Google Scholar
  333. Hald, A. (2007). A history of parametric statistical inference from Bernoulli to Fisher, 1713–1935. New York: Springer.Google Scholar
  334. 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
  335. Halpern, J. Y. (1999a). A counterexample of to theorems of Cox and Fine. The Journal of Artificial Intelligence Research, 10, 67–85.Google Scholar
  336. Halpern, J. Y. (1999b). Technical addendum, Cox’s theorem revisited. The Journal of Artificial Intelligence Research, 11, 429–435.Google Scholar
  337. 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
  338. Halpern, J. Y., & Koller, D. (2004). Representation dependence in probabilistic inference. The Journal of Artificial Intelligence Research, 21, 319–356.Google Scholar
  339. Hanson, T. E. (2006). Modeling censored lifetime data using a mixture of Gamma baseline. Bayesian Analysis, 1(3), 575–594.Google Scholar
  340. 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
  341. Harr, A. (1933). Der massbegrieff in der Theorie der kontinuierlichen Gruppen. Annals of Mathematical Statistics, 34, 147–169.Google Scholar
  342. 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
  343. Hasselblad, V. (1966). Estimation of parameters for a mixture of normal distribution. Technometrics, 8(3), 431–444.Google Scholar
  344. 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
  345. 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
  346. Heckman, J., & Singer, B. (1984a). Econometric duration analysis. Journal of Econometrics, 24, 63–132.Google Scholar
  347. Heckman, J., & Singer, B. (1984b). A method for minimizing distributional assumptions in econometric models for duration data. Econometrica, 52(2), 271–320.Google Scholar
  348. Hempel, C. G., & Oppenheim, P. (1948). Studies in the logic explanation. Philosophy of Science, 15, 567–579.Google Scholar
  349. Henry, L. (1957). Un exemple de surestimation de la mortalité par la méthode de Halley. Population, 12(1), 141–142.Google Scholar
  350. Henry, L. (1959). D’un problème fondamental de l’analyse démographique. Population, 14(1), 9–32.Google Scholar
  351. Henry, L. (1966). Analyse et mesure des phénomènes démographiques par cohorte. Population, 13(1), 465–482.Google Scholar
  352. Henry, L. (1972). Démographie. Analyse et modèles. Paris: Larousse.Google Scholar
  353. 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
  354. Henry, L., & Blayo, Y. (1975). La population de la France de 1740 à 1860. Population, 30, 71–122.Google Scholar
  355. 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
  356. Hilbert, D. (1902). Mathematical problems. Bulletin of the American Mathematical Society, 8(2), 437–439.Google Scholar
  357. Hoem, J. (1983). Multistate mathematical demography should adopt the notions of event history analysis. Stockholm Research Reports in Demography, 10, 1–17.Google Scholar
  358. 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
  359. Holland, P. (1986). Statistics and causal inference (with comments). Journal of the American Statistical Association, 81, 945–970.Google Scholar
  360. 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
  361. 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
  362. 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
  363. Hoppa, R. D., & Vaupel, J. W. (Eds.). (2002). Paleodemography. Age distribution from skeletons samples. Cambridge, UK: Cambridge University Press.Google Scholar
  364. 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
  365. Hunt, G. A. (1966). Martingales et processus de Markov. Paris: Dunod.Google Scholar
  366. Hunter, D. (1989). Causality and maximum entropy updating. International Journal of Approximate Reasoning, 3, 87–114.Google Scholar
  367. 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
  368. Huygens, C. (1657). De ratiociniis in ludo aleae. Leyde: Elzevier.Google Scholar
  369. Huygens, C. (1895). Correspondance 1666–1669. In Œuvres complètes (Vol. Tome Sixième). La Haye: Martinus Nijhoff.Google Scholar
  370. Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian survival analysis. New York/Berlin/Heidelberg: Springer.Google Scholar
  371. 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
  372. Illari, P. M., Russo, F., & Williamson, J. (Eds.). (2011). Causality in the sciences. Oxford: Oxford University Press.Google Scholar
  373. 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
  374. Irwin, J. O. (1941). Book review of Jeffreys’ Theory of probability. Journal of the Royal Statistical Society, 104, 59–64.Google Scholar
  375. Jacob, F. (1970). La logique du vivant. Paris: Gallimard.Google Scholar
  376. Jacob, P. (1980). L’empirisme logique: ses antécédents, ses critiques. Paris: Editions de Minuit.Google Scholar
  377. Jaynes, E. T. (1956). Probability theory in science and engineering. Dallas: Socony-Mobil Oil Co.Google Scholar
  378. 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
  379. Jaynes, E. T. (1963). Information theory and statistical mechanics. In K. Ford (Ed.), Statistical physics (pp. 181–218). New York: Benjamin.Google Scholar
  380. Jaynes, E. T. (1968). Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC-4, 227–241.Google Scholar
  381. 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
  382. 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
  383. 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
  384. 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
  385. 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
  386. 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.
  387. Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.Google Scholar
  388. Jeffreys, H. (1931). Scientific inference. Cambridge, UK: Cambridge University Press.Google Scholar
  389. Jeffreys, H. (1932). On the theory of errors and least squares. Proceedings of the Royal Society, Series A, 138, 48–55.Google Scholar
  390. Jeffreys, H. (1933a). Probability, statistics, and the theory of errors. Proceedings of the Royal Society, Series A, 140, 523–535.Google Scholar
  391. Jeffreys, H. (1933b). On the prior probability in the theory of sampling. Proceedings of the Cambridge Philosophical Society, 29, 83–87.Google Scholar
  392. Jeffreys, H. (1934). Probability and scientific method. Proceedings of the Royal Society, Series A, 146, 9–16.Google Scholar
  393. 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
  394. Jeffreys, H. (1939). Theory of probability. New York: Clarendon Press.Google Scholar
  395. 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
  396. Jeffreys, H. (1955). The present position in probability theory. The British Journal for the Philosophy of Science, 5, 275–289.Google Scholar
  397. Johnson, W. E. (1932). Probability: The deductive and inductive problem. Mind, 41, 409–423.Google Scholar
  398. Jones, K. (1993). ‘Everywhere is nowhere’: Multilevel perspectives on the importance of place. Portsmouth: The University of Portsmouth Inaugural Lectures.Google Scholar
  399. 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.
  400. Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgement of representativeness. Cognitive Psychology, 3, 430–454.Google Scholar
  401. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.Google Scholar
  402. 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
  403. Kalbfleisch, J. D., & Prentice, R. L. (1980). The statistical analysis of failure time data. New York/Chichester/Brisbane/Toronto: Wiley.Google Scholar
  404. Kamke, E. (1932). Über neure Begründungen der Wahrscheinlichkeitsrechnung. Jahresbericht der Deutschen Mathematiker-Vereinigung, 42, 14–27.Google Scholar
  405. Kaplan, M. (1996). Decision theory as philosophy. Cambridge, MA: Cambridge University Press.Google Scholar
  406. Kaplan, E. L., & Meier, P. (1958). Non-parametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457–481.Google Scholar
  407. 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
  408. Karlis, D., & Patilea, V. (2007). Confidence hazard rate functions for discrete distributions using mixtures. Computational Statistics & Data Analysis, 51(11), 5388–5401.Google Scholar
  409. 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
  410. Kaufmann, W. (1906). Über die Konstitution des Elektrons. Annalen der Physik, 324(3), 487–553.Google Scholar
  411. Keiding, N. (1990). Statistical inference in the Lexis diagram. Philosophical Transactions of the Royal Society of London, 332, 487–509.Google Scholar
  412. 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
  413. 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
  414. Kendall, M. G. (1963). Ronald Aylmer Fisher, 1890–1962. Biometrika, 50(1/2), 1–15.Google Scholar
  415. 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
  416. Keynes, J. M. (1921). A treatise on probability. London: Macmillan.Google Scholar
  417. Keynes, J. M. (1971). Essay in biography. In The collected writings of John Maynard Keynes (Vol. X). London: Macmillan.Google Scholar
  418. Kimura, D. K. (1977). Statistical assessment of the age-length key. Journal of the Fisheries Research Board of Canada, 34, 317–324.Google Scholar
  419. Kimura, D. K., & Chikuni, S. (1987). Mixture of empirical distributions: An iterative application of the age-length key. Biometrics, 43, 23–35.Google Scholar
  420. Klotz, L. H. (1999). Is the rate of testicular cancer increasing? Canadian Medical Association Journal, 160, 213–214.Google Scholar
  421. 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
  422. 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
  423. 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
  424. Knuth, K. H. (2005). Lattice duality: The origin of probability and entropy. Neurocomputing, 67, 245–274.Google Scholar
  425. 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
  426. 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
  427. Knuth, K. H. (2009). Measuring on lattices. Arxiv preprint: arXiv0909.3684v1 (math..GM). Accessed July 11, 2011.Google Scholar
  428. Knuth, K. H. (2010a). Foundations of inference. Arxiv preprint: arXiv1008.4831v1 (math..PR). Accessed July 11, 2011.Google Scholar
  429. Knuth, K. H. (2010b). Information physics: The new frontier. Arxiv preprint: arXiv1009. 5161v1 (math.ph). Accessed July 11, 2011.Google Scholar
  430. 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
  431. Kolmogorov, A. (1951). Bepoятнocть (Probability). In Great Soviet encyclopedia (Vol. 7, pp. 508–510). Moscow: Soviet Encyclopedia Publishing House.Google Scholar
  432. Kolmogorov, A. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1(1), 1–7.Google Scholar
  433. Konigsberg, L. W., & Frankenberg, S. R. (1992). Estimation of age structure in anthropological demography. American Journal of Physical Anthropology, 89, 235–256.Google Scholar
  434. Konigsberg, L. W., & Frankenberg, S. R. (2002). Deconstructing death in paleodemography. American Journal of Physical Anthropology, 117, 297–309.Google Scholar
  435. 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
  436. Koopman, B. O. (1940). The axioms and algebra of intuitive probability. Annals of Mathematics, 41(2), 269–292.Google Scholar
  437. Koopman, B. O. (1941). Intuitive probabilities and sequences. Annals of Mathematics, 42(1), 169–187.Google Scholar
  438. Kraft, C. H., Pratt, J. W., & Seidenberg, A. (1959). Intuitive probability on finite sets. Annals of Mathematical Statistics, 30, 408–419.Google Scholar
  439. Krüger, L., Daston, L. J., & Heidelberg, M. (Eds.). (1986). The probabilistic revolution. Cambridge, UK: Cambridge University Press.Google Scholar
  440. Kruithof, R. (1937). Telefoonverkeersreking. De Ingenieur, 52(8), E15–E25.Google Scholar
  441. Kuhn, T. (1962). The structure of scientific revolutions. Chicago/London: The University of Chicago Press.Google Scholar
  442. 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
  443. 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
  444. Kullbach, S., & Leiber, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.Google Scholar
  445. 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
  446. Kyburg, H. (1978). Subjective probability: Criticisms, reflections and problems. Journal of Philosophical Logic, 7, 157–180.Google Scholar
  447. La Harpe, J.-F. (1799). Lycée, ou cours de littérature ancienne et moderne (Vol. 14). Paris: Chez H. Agasse (An VII).Google Scholar
  448. Laemmel, R. (1904). Untersuchungen über die Ermittlung von Wahrscheinlichkeiten. PhD thesis, Universität Zürich, Zurich.Google Scholar
  449. 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
  450. Lambert, J. H. (1772). Beyträge zum Gebrauche der Mathematik und deren Anwendung (Vol. III). Berlin: Verlag der Buchhandlung der Realschule.Google Scholar
  451. Landry, A. (1909). Les trois théories principales de la population. Scientia, 6, 1–29.Google Scholar
  452. Landry, A. (1945). Traité de démographie. Paris: Payot.Google Scholar
  453. 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
  454. Laplace, P. S. (1778). Mémoire sur les probabilités. Mémoires de l’Académie Royale des sciences de Paris, 1781, 227–332.Google Scholar
  455. 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
  456. 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
  457. 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
  458. 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
  459. 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
  460. Laplace, P. S. (1812). Théorie analytique des probabilités (Vols. 2). Paris: Courcier Imprimeur.Google Scholar
  461. Laplace, P. S. (1814). Essai philosophique sur les probabilités. Paris: Courcier Imprimeur.Google Scholar
  462. 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
  463. 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
  464. Lazarsfeld, P. (Ed.). (1954). Mathematical thinking in the social science. Glencoe: The Free Press.Google Scholar
  465. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Hougthton Mifflin.Google Scholar
  466. 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
  467. Le Bras, H. (1971). Géographie de la fécondité française depuis 1921. Population, 26(6), 1093–1124.Google Scholar
  468. Le Bras, H. (2000). Naissance de la mortalité. L’origine politique de la statistique et de la démographie. Paris: Seuil/Gallimard.Google Scholar
  469. Lebesgue, H. (1901). Sur une généralisation de l’intégrale définie. Comptes Rendus de l’Académie des Sciences, 132, 1025–1028.Google Scholar
  470. Lecoutre, B. (2004). Expérimentation, inférence statistique et analyse causale. Intellectica, 38(1), 193–245.Google Scholar
  471. Lee, P. M. (1989). Bayesian statistics. London: Arnold.Google Scholar
  472. Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Paris: Coursier.Google Scholar
  473. 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.
  474. Leibniz, G. W. (1666). Dissertatio de arte combinatoria. Leipzig (French translation: Peyroux, J. (1986). Dissertation sur l’art combinatoire. Paris: Blanchard).Google Scholar
  475. 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
  476. Leibniz, G. W. (1765). Nouveaux essais sur l’entendement humain. Paris: GF-Flammarion.Google Scholar
  477. 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
  478. Leonard, T., & Hsu, S. J. (1999). Bayesian methods. An analysis for statisticians and interdisciplinary researchers. Cambridge, UK: Cambridge University Press.Google Scholar
  479. Lévy, P. (1925). Calcul des probabilités. Paris: Gauthier-Villars.Google Scholar
  480. Lévy, P. (1936). Sur quelques points de la théorie des probabilités dénombrables. Annales de l’Institut Henri Poincaré, 6(2), 153–184.Google Scholar
  481. Lévy, P. (1937). Théorie de l’addition des variables aléatoires. Paris: Gauthier-Villars.Google Scholar
  482. Lewis, D. K. (1973a). Causation. The Journal of Philosophy, 70, 556–567.Google Scholar
  483. Lewis, D. K. (1973b). Counterfactuals. Oxford: Basil Blackwell.Google Scholar
  484. Lexis, W. (1877). Zur Theorie der Massenerscheinungen in der menschlichen Gesellschaft. Freiburg: Wagner.Google Scholar
  485. Lexis, W. (1879). Über die Theorie der Stabilität statistischer Reihen. Jahrbücher für Nationalökonomie und Statistik, 32, 60–98.Google Scholar
  486. 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
  487. Lillard, L. A. (1993). Simultaneous equations fir hazards: Marriage duration and fertility timing. Journal of Econometrics, 56, 189–217.Google Scholar
  488. Lindley, D. V. (1956). On a measure of information provided by an experiment. Annals of Mathematical Statistics, 27(4), 986–1005.Google Scholar
  489. 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
  490. Lindley, D. V. (1977). A problem in forensic science. Biometrika, 64(2), 207–213.Google Scholar
  491. Lindley, D. V. (2000). The philosophy of statistics. Journal of the Royal Statistical Society, Series D (The Statistician), 49(3), 293–337.Google Scholar
  492. Lindley, D. V., & Novick, M. R. (1981). The role of exchangeability in inference. The Annals of Statistics, 9, 45–58.Google Scholar
  493. 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
  494. Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. Hayward: Institute of Mathematical Statistics.Google Scholar
  495. Little, D. (2010). New contributions to the philosophy of history (Methodos series, Vol. 6). Dordrecht/Heidelberg/London/New York: Springer.Google Scholar
  496. Łomnicki, A. (1923). Nouveaux fondements du calcul des probabilités. Fundamenta Mathematicae, 4, 34–71.Google Scholar
  497. Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92, 805–824.Google Scholar
  498. Lotka, A. J. (1939). Théorie analytique des associations biologiques; Deuxième partie. Analyse démographique avec application particulière à l’espèce humaine. Paris: Herman.Google Scholar
  499. Louçã, F. (2007). The years of high econometrics: A short history of the generation that reinvented economics. London/New York: Routledge.Google Scholar
  500. Luce, R. D., & Krantz, D. H. (1971). Conditional expected utility. Econometrica, 39(2), 253–271.Google Scholar
  501. 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
  502. Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.Google Scholar
  503. Machina, M. J. (1982). “Expected utility” analysis without the independence axiom. Econometrica, 50(2), 277–323.Google Scholar
  504. 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
  505. Madden, E. H. (1969). A third view of causality. The Review of Metaphysics, XXIII, 67–84.Google Scholar
  506. Mandel, D. R. (2005). Are risk assessments of a terrorist attack coherent? Journal of Experimental Psychology Applied, 11, 277–288.Google Scholar
  507. Mandel, D. R. (2008). Violations of coherence in subjective probability: A representational and assessment processes account. Cognition, 106, 130–156.Google Scholar
  508. Manski, C. F., & McFadden, D. L. (1981). Structural analysis of discrete data and econometric applications. Cambridge, UK: The MIT Press.Google Scholar
  509. 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
  510. March, L. (1908). Remarques sur la terminologie en statistique. In Congrès de mathématiques de Rome, JSSP, pp. 290–296.Google Scholar
  511. 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
  512. Martin-Löf, P. (1966). The definition of random sequences. Information and Control, 7, 602–619.Google Scholar
  513. 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
  514. Masset, C. (1982). Estimation de l’âge au décès par les sutures crâniennes. PhD thesis, University Paris VII, Paris.Google Scholar
  515. Masset, C. (1995). Paléodémographie: problèmes méthodologiques. Cahiers d’Anthropologie et Biométrie Humaine, XIII(1–2), 27–38.Google Scholar
  516. 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
  517. Matalon, B. (1967). Epistémologie des probabilités. In J. Piaget (Ed.), Logique et connaissance scientifique (pp. 526–553). Paris: Gallimard.Google Scholar
  518. 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
  519. 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
  520. 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
  521. 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
  522. 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
  523. McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.Google Scholar
  524. Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. W., III. (1972). The limits of growth. New York: Universe Books.Google Scholar
  525. 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
  526. 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
  527. Meyer, P.-A. (1972). Martingales and stochastic integrals. Berlin/Heidelberg/New York: Springer.Google Scholar
  528. 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
  529. Missiakoulis, S. (2010). Cecrops, King of Athens: The first (?) recorded population census in history. International Statistical Review, 78(3), 413–418.Google Scholar
  530. Moheau, M. (1778). Recherches et considérations sur la population de la France. Edition annotée par Vilquin E., Paris: INED PUF.Google Scholar
  531. Mongin, P. (2003). L’axiomatisation et les théories économiques. Revue Economique, 54(1), 99–138.Google Scholar
  532. Morrison, D. (1967). On the consistency of preferences in Allais’s paradox. Behavioral Science, 12, 373–383.Google Scholar
  533. Mosteller, F., & Wallace, D. L. (1964). Applied Bayesian and classical inference: The case of the federalist papers. New York: Springer.Google Scholar
  534. Muliere, P., & Parmigiani, G. (1993). Utility and means in the 1930s. Statistical Science, 8(4), 421–432.Google Scholar
  535. 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
  536. Nadeau, R. (1999). Vocabulaire technique et analytique de l’épistémologie. Paris: Presses Universitaires de France.Google Scholar
  537. Nagel, E. (1940). Book review of Jeffreys’ Theory of probability. The Journal of Philosophy, 37, 524–528.Google Scholar
  538. Nagel, E. (1961). The structure of science. London: Routledge and Kegan Paul.Google Scholar
  539. Narens, L. (1976). Utility, uncertainty and trade-off structures. Journal of Mathematical Psychology, 13, 296–332.Google Scholar
  540. 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
  541. Neveu, J. (1972). Martingales à temps discret. Paris: Masson.Google Scholar
  542. Newton, I. (1687). Philosophia naturalis principia mathematica. Londini: S. Pepys.Google Scholar
  543. 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
  544. Neyman, J. (1940). Book review of Jeffreys’ Theory of probability. Journal of the American Statistical Association, 35, 558–559.Google Scholar
  545. 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
  546. 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
  547. 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
  548. 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
  549. 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
  550. Paris, J. B. (1994). The uncertain reasoner’s companion. A mathematical perspective. Cambridge, UK: Cambridge University Press.Google Scholar
  551. Paris, J., & Vencovská, A. (1997). In defence of maximum entropy inference process. International Journal of Approximate Reasoning, 17(1), 77–103.Google Scholar
  552. 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
  553. Pascal, B. (1640). Essay sur les coniques. B.N. Imp. Res. V 859.Google Scholar
  554. 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
  555. Pascal, B. (1648). Récit de l’expérience de l’équilibre des liqueurs. Paris: C. Savreux.Google Scholar
  556. Pascal, B. (1654a). Traité du triangle arithmétique, avec quelques autres traités sur le même sujet. Paris: Guillaume Desprez.Google Scholar
  557. Pascal, B. (1654b). Celeberrimæ mathesos academiæ Pariensi. Paris: Académie Parisienne.Google Scholar
  558. Pascal, B. (1670). Pensées. Paris: Édition de Port-Royal.Google Scholar
  559. 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
  560. Pasch, M. (1882). Vorlesungen über neure Geometrie. Leipzig: Verlag from Julius Springer.Google Scholar
  561. Patil, G. P., & Rao, C. R. (1994). Environmental statistics. Amsterdam: Elsevier Science B.V.Google Scholar
  562. 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
  563. Pearl, J. (1988). Probabilistic reasoning and intelligent systems: Networks of plausible inference. San Mateo: Morgan Kaufmann.Google Scholar
  564. Pearl, J. (1995). Causal diagrams for empirical research (with discussion). Biometrika, 82(4), 669–710.Google Scholar
  565. Pearl, J. (2000). Causality, reasoning and inference. Cambridge, UK: Cambridge University Press.Google Scholar
  566. 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
  567. 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
  568. 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
  569. 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
  570. Pearson, K. (1911). The grammar of science. London: Adam and Charles Black.Google Scholar
  571. Pearson, K. (1920). The fundamental problem of practical statistics. Biometrika, 13(1), 1–16.Google Scholar
  572. Pearson, K. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika, 17(3/4), 388–442.Google Scholar
  573. 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
  574. 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
  575. Petty, W. (1690). Political arithmetick. London: Robert Clavel & Hen. Mortlock.Google Scholar
  576. 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
  577. 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.
  578. 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.
  579. 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.
  580. Poincaré, H. (1912). Calcul des probabilités. Paris: Gauthier-Villars.Google Scholar
  581. 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
  582. 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
  583. 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
  584. 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
  585. Poisson, S.-D. (1837). Recherches sur la probabilité des jugements en matière criminelle et en matière civile. Paris: Bachelier.Google Scholar
  586. Polya, G. (1954). Mathematics and plausible reasoning (2 vols). Princeton: Princeton University Press.Google Scholar
  587. Popper, K. (1934). Logik der forshung. Vienna: Springer.Google Scholar
  588. 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
  589. Popper, K. (1959). The propensity interpretation of probability. Philosophy of Science, 10, 25–42.Google Scholar
  590. Popper, K. (1982). The postscript to The logic of scientific discovery III. Quantum theory and the schism in physics. London: Hutchinson.Google Scholar
  591. Popper, K. (1983). The postscript of the logic of scientific discovery. I. Realism and the aim of science. London: Hutchinson.Google Scholar
  592. Porter, T. M. (1986). The rise of statistical thinking 1820–1900. Princeton: Princeton University Press.Google Scholar
  593. 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
  594. 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
  595. Pratt, D. (2010). Modeling written communication. A new systems approach to modelling in the social sciences. Dordrecht/Heidelberg/London/New York: Springer.Google Scholar
  596. Prentice, R. L. (1978). Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika, 69, 167–179.Google Scholar
  597. Pressat, R. (1966). Principes d’analyse. Paris: INED.Google Scholar
  598. 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
  599. Preston, S. H., & Coale, A. J. (1982). Age/structure growth, attrition and accession: A new synthesis. Population Index, 48(2), 217–259.Google Scholar
  600. 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
  601. 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
  602. 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
  603. Rabinovitch, N. L. (1969). Studies in the history of probability and statistics. XXII. Probability in the Talmud. Biometrika, 56(2), 437–441.Google Scholar
  604. 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
  605. Radon, J. (1913). Theorie und Anwendungen der absolut Additiven Mengenfunktionen. Sitzungsberichte der kaiserlichen Akademie der Wissenschaften, Mathematisch-Naturwissenschaftliche Klasse 122IIa, pp. 1295–1438.Google Scholar
  606. Railton, P. (1978). A deductive-nomological model of probabilistic explanation. Philosophy of Science, 45, 206–226.Google Scholar
  607. Ramsey, F. P. (1922). Mr. Keynes and probability. The Cambridge Magazine, 11(1), 3–5. Google Scholar
  608. 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
  609. 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
  610. 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
  611. 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
  612. Reichenbach, H. (1937). Les fondements logiques du calcul des probabilités. Annales de l’Institut Henri Poincaré, 7, 267–348.Google Scholar
  613. Remenik, D. (2009). Limit theorems for individual-based models in economics and finance. arXiv: 0812813v4 [math.PR], 38 p.Google Scholar
  614. Reungoat, S. (2004). William Petty. Observateur des Îles Britanniques. Classiques de l’économie et de la population. Paris: INED.Google Scholar
  615. 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
  616. 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
  617. 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.
  618. 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
  619. Robert, C. P. (2006). Le choix bayésien. Paris: Springer.Google Scholar
  620. Robert, C. P., Chopin, N., & Rousseau, J. (2009). Harold Jeffreys’s theory of probability revisited. Statistical Science, 24(2), 141–172.Google Scholar
  621. 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
  622. Robertson, B., & Vignaux, G. A. (1991). Inferring beyond reasonable doubt. Oxford Journal of Legal Studies, 11(3), 431–438.Google Scholar
  623. Robertson, B., & Vignaux, G. A. (1993). Probability – The logic of the law. Oxford Journal of Legal Studies, 13(4), 457–478.Google Scholar
  624. Robertson, B., & Vignaux, G. A. (1995). Interpreting evidence: Evaluation forensic science in the courtroom. New York/Chichester/Brisbane/Toronto: Wiley.Google Scholar
  625. Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357.Google Scholar
  626. Roehner, B., & Syme, T. (2002). Pattern and repertoire in history. Cambridge, MA: Harvard University Press.Google Scholar
  627. 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
  628. 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
  629. 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
  630. 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
  631. Royall, R. M. (1970). On finite population theory under certain linear regression models. Biometrika, 57(2), 377–387.Google Scholar
  632. 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
  633. Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Psychology, 2, 1–26.Google Scholar
  634. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6, 34–58.Google Scholar
  635. Russo, F. (2009). Causality and causal modelling in the social sciences. Measuring variations (Methodos series, Vol. 5). Dordrecht: Springer.Google Scholar
  636. 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
  637. Ryder, N. B. (1954). La mesure des variations de la fécondité au cours du temps. Population, 11(1), 29–46.Google Scholar
  638. Ryder, N. B. (1964). Notes on the concept of population. The American Journal of Sociology, 69(5), 447–463.Google Scholar
  639. 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
  640. 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
  641. Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.Google Scholar
  642. Salmon, W. C. (1991). Hans Reichenbach’s vindication of induction. Erkenntnis, 35(1–3), 99–122.Google Scholar
  643. Sarkar, S. (Ed.). (1996). Decline and obsolescence of logical empirism: Carnap vs. Quine and the critics. New York/London: Garland Publishing, Inc.Google Scholar
  644. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.Google Scholar
  645. Savage, L. J. (1962). The foundations of statistical inference. New York: Wiley.Google Scholar
  646. Savage, L. J. (1967). Implications of personal probability for induction. The Journal of Philosophy, 64(19), 593–607.Google Scholar
  647. Savage, L. J. (1976). On reading R.A. Fisher. The Annals of Statistics, 4(3), 441–500.Google Scholar
  648. Schmid, J., & Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 83–90.Google Scholar
  649. Schmitt, R. C., & Crosetti, A. H. (1954). Accuracy of the ratio-correlation method for estimating postcensal population. Land Economics, 30, 279–281.Google Scholar
  650. Scott, D. (1964). Measurement structures and linear inequalities. Journal of Mathematical Psychology, 1, 233–247.Google Scholar
  651. Séguy, I., & Buchet, L. (Eds.). (2011). Manuel de paléodémographie. Paris: INED.Google Scholar
  652. 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
  653. 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
  654. Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.Google Scholar
  655. Shafer, G. (1979). Allocations of probability. Annals of Probability, 7(5), 827–839.Google Scholar
  656. Shafer, G. (1982). Thomas Bayes’s Bayesian inference. Journal of the Royal Statistical Society, Series A, 145(2), 250–258.Google Scholar
  657. Shafer, G. (1985). Conditional probability. International Statistical Review, 53(3), 261–277.Google Scholar
  658. Shafer, G. (1986). Savage revisited. Statistical Science, 1(4), 463–501.Google Scholar
  659. Shafer, G. (1990a). The unity and diversity of probability (with comments). Statistical Science, 5(4), 435–462.Google Scholar
  660. 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
  661. 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
  662. Shafer, G. (1996). The art of causal conjecture. Cambridge, MA: MIT Press.Google Scholar
  663. 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
  664. 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
  665. 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.
  666. Shafer, G., & Pearl, J. (Eds.). (1990). Readings in uncertainty reasoning. San Mateo: Morgan Kaufman Publishers.Google Scholar
  667. Shafer, G., & Volk, V. (2006). The sources of Kolmogorov Grundbegriffe. Statistical Science, 21(1), 70–98.Google Scholar
  668. Shafer, G., & Vovk, V. (2001). Probability and finance: It’s only a game! New York: Wiley.Google Scholar
  669. 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.
  670. Shafer, G., Gilett, P. R., & Scherl, R. B. (2000). The logic of events. Annals of Mathematics and Artificial Intelligence, 28, 315–389.Google Scholar
  671. Shalizi, C. R. (2009). Dynamics of Bayesian updating with dependent data and misspecified models. Electronic Journal of Statistics, 3, 1039–1074.Google Scholar
  672. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.Google Scholar
  673. 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
  674. Simpson, E. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B, 13, 238–241.Google Scholar
  675. Sinha, D. (1993). Semiparametric Bayesian analysis of multiple even time data. Journal of the American Statistical Association, 88(423), 979–983.Google Scholar
  676. 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
  677. Skilling, J. (1998). Probabilistic data analysis: An introductory guide. Journal of Microscopy, 190(1–2), 28–36.Google Scholar
  678. Slovic, P., & Tversky, A. (1974). Who accepts Savage’s axioms? Behavioral Science, 19, 368–373.Google Scholar
  679. 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
  680. 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
  681. 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
  682. Smets, P. (1991). Probability of provability and belief functions. Logique et Analyse, 133–134, 177–195.Google Scholar
  683. 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
  684. Smets, P. (1997). The normative representation of quantified beliefs by belief functions. Artificial Intelligence, 92, 229–242.Google Scholar
  685. 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
  686. Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2), 191–234.Google Scholar
  687. Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. London: W Strahan and T. Cadell.Google Scholar
  688. 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
  689. 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
  690. Smith, H. L. (1990). Specification problems in experimental and nonexperimental social research. Sociological Methodology, 20, 59–91.Google Scholar
  691. 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
  692. 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
  693. 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
  694. Smith, R. C., & Crosetti, A. H. (1954). Accuracy of ratio-correlation method for estimating postcensal population. Land Economics, 30(3), 279–280.Google Scholar
  695. Snow, P. (1998). On the correctness and reasonableness of Cox’s theorem for finite domains. Computational Intelligence, 14(3), 452–459.Google Scholar
  696. 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
  697. Solomonoff, R. J. (1960). A preliminary report on a general theory of inductive inference (Report ZTB-138). Cambridge, MA: Zator CO.Google Scholar
  698. Solomonoff, R. J. (1964a). A formal theory of inductive inference, Part 1. Information and Control, 7(1), 1–22.Google Scholar
  699. Solomonoff, R. J. (1964b). A formal theory of inductive inference, Part 2. Information and Control, 7(2), 224–254.Google Scholar
  700. 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
  701. Solomonoff, R. J. (1997). The discovery of algorithmic probability. Journal of Computer and System Science, 55(1), 73–88.Google Scholar
  702. Spearman, C. (1904). “General intelligence”, objectively determined and measured. The American Journal of Psychology, 15, 201–293.Google Scholar
  703. 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
  704. 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
  705. Steinhaus, H. (1923). Les probabilités dénombrables et leur rapport à la théorie de la mesure. Fundamenta Mathematicae, 4, 286–310.Google Scholar
  706. 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
  707. 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
  708. 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
  709. 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
  710. Stigler, S. M. (1982). Thomas Bayes Bayesian inference. Journal of the Royal Statistical Society, Series A, 145, 250–258.Google Scholar
  711. Stigler, S. M. (1986). The history of statistics: the measurement of uncertainty before 1900. Cambridge, MA: Belknap Press of Harvard University Press.Google Scholar
  712. Stigum, B. P. (1972). Finite state space and expected utility maximization. Econometrica, 40, 253–259.Google Scholar
  713. 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
  714. Suppe, F. (1989). The semantic conception of theories and scientific realism. Urbana/Chicago: University of Illinois Press.Google Scholar
  715. 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
  716. 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
  717. Suppes, P. (1970). A probabilistic theory of causality. Amsterdam: North Holland.Google Scholar
  718. Suppes, P. (1974). The measurement of belief. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), 160–191.Google Scholar
  719. 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
  720. Suppes, P. (2002a). Representation and invariance of scientific structures. Stanford: CSLI Publications.Google Scholar
  721. Suppes, P. (2002b). Representation of probability. In P. Suppes (Ed.), Representation and invariance of scientific structures (pp. 129–264). Stanford: CSLI Publications.Google Scholar
  722. 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
  723. Suppes, P., & Zanotti, M. (1982). Necessary and sufficient qualitative axioms for conditional probability. Zeitschrift für Wahrscheinlichkeitstheorie und Werwande Gebiete, 60, 163–169.Google Scholar
  724. 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
  725. Susser, M. (1996). Choosing a future for epidemiology: I. Eras and paradigms. American Journal of Public Health, 86(5), 668–673.Google Scholar
  726. 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
  727. 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
  728. 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
  729. 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
  730. Thomas, D. H. (1986). Refiguring anthropology: First principles of probability & statistics. Long Grove: Waveland Press, Inc.Google Scholar
  731. Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286.Google Scholar
  732. Thurstone, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.Google Scholar
  733. Thurstone, L. L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.Google Scholar
  734. Tinbergen, J. (1939). Vérification statistique des théories de cycles économiques. Une méthode d’application au mouvement des investissements. Genève: SDN.Google Scholar
  735. Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1985). Statistical analysis of finite mixtures distributions. New York: Wiley.Google Scholar
  736. 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
  737. Torche, F. (2011). The effect of maternal stress on birth outcomes: Exploiting a natural experiment. Demography, 48(11), 1473–1491.Google Scholar
  738. Tornier, E. (1929). Wahrscheinlichkeisrechnunug und zalhlentheorie. Journal für die Teine und Angewandte Mathematik, 60, 177–198.Google Scholar
  739. 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
  740. 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
  741. 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
  742. Tuma, N. B., & Hannan, M. (1984). Social dynamics. London: Academic.Google Scholar
  743. Turing, A. M. (1936). On computable numbers, with an application to Endscheidungsproblem. Proceedings of the London Mathematical Society, 42(2), 230–265.Google Scholar
  744. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.Google Scholar
  745. Tversky, A. (1974). Assessing uncertainty. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), 148–159.Google Scholar
  746. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(30), 453–457.Google Scholar
  747. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.Google Scholar
  748. Tversky, A., & Koeler, D. J. (1994). Support theory: A nonextensional representation of subjective probability. Psychological Review, 101, 547–567.Google Scholar
  749. 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
  750. Van Fraassen, B. (1980). The scientific image. Oxford: Clarendon Press.Google Scholar
  751. 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
  752. 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
  753. van Lambalgen, M. (1987). Von Mises’ definition of random sequences reconsidered. The Journal of Symbolic Logic, 32(3), 725–755.Google Scholar
  754. 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
  755. 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
  756. Venn, J. (1866). The logic of chance. London: Macmillan.Google Scholar
  757. Vernon, P. E. (1950). The structure of human abilities. London: Methuen.Google Scholar
  758. 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
  759. 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
  760. 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
  761. Vidal, A. (1994). La pensée démographique. Doctrines, théories et politiques de population. Grenoble: Presses Universitaires de Grenoble.Google Scholar
  762. 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
  763. Ville, J. A. (1939). Étude critique de la notion de collectif. Paris: Gauthier-Villars.Google Scholar
  764. Vilquin, E. (1977). Introduction. In J. Graunt (Ed.), Observations naturelles et politiques (pp. 7–31). Paris: INED.Google Scholar
  765. 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
  766. von Mises, R. (1919). Grundlagen der wahrscheinlichkeitesrechnung. Mathematische Zeitschrift, 5, 52–99.Google Scholar
  767. von Mises, R. (1928). Wahrscheinlichkeit, Statistik und Wahrheit. Wien: Springer (English translation: (1957). Probability, statistics and truth. London: George Allen & Unwin Ltd.).Google Scholar
  768. von Mises, R. (1932). Théorie des probabilités. Fondements et applications. Annales de l’Institut Henri Poincaré, 3(2), 137–190.Google Scholar
  769. von Mises, R. (1942). On the correct use of Bayes’s formula. Annals of Mathematical Statistics, 13, 156–165.Google Scholar
  770. von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behaviour. Princeton: Princeton University Press.Google Scholar
  771. Von Wright, G. H. (1971). Explanation and understanding. London: Routledge and Kegan Paul.Google Scholar
  772. 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
  773. 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.
  774. Waismann, F. (1930). Logische Analyse des wahrscheinlichkeisbegriffs. Erkenntnis, 1, 228–248.Google Scholar
  775. 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
  776. Wald, A. (1947a). Foundations of a general theory of sequential decision functions. Econometrica, 15(4), 279–313.Google Scholar
  777. Wald, A. (1947b). Sequential analysis. New York: Wiley.Google Scholar
  778. Wald, A. (1949). Statistical decision functions. Annals of Mathematical Statistics, 20(2), 165–205.Google Scholar
  779. Wald, A. (1950). Statistical decision functions. New York: Wiley.Google Scholar
  780. 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
  781. Wargentin, P. W. (1766). Mortaliteten i Sverige, i anledning af Tabell-Verket. Kongl. Svenska Vetenskap Academiens Handlingar, XXVII, 1–25.Google Scholar
  782. Wavre, R. (1938–1939). Colloque consacré à la théorie des probabilités, Fascicules 734–740; 766. Paris: Hermann.Google Scholar
  783. Weber, M. (1998). The resilience of the Allais paradox. Ethics, 109(1), 94–118.Google Scholar
  784. Weber, B. (2007). The effects of losses and event splitting on the Allais paradox. Judgment and Decision Making, 2, 115–125.Google Scholar
  785. Whelpton, P. (1946). Reproduction rates adjusted for age, parity, fecundity and marriage. Journal of the American Statistical Association, 41, 501–516.Google Scholar
  786. Whelpton, P. (1949). Cohort analysis of fertility. American Sociological Review, 14(6), 735–749.Google Scholar
  787. 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
  788. Wilks, S. S. (1941). Book review of Jeffreys’ Theory of probability. Biometrika, 32, 192–194.Google Scholar
  789. Williamson, J. (2005). Bayesian nets and causality: Philosophical and computational foundations. Oxford: Oxford University Press.Google Scholar
  790. 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
  791. Wilson, M. C. (2007). Uncertainty and probability in institutional economics. Journal of Economic Issues, 41(4), 1087–1108.Google Scholar
  792. 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
  793. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar
  794. Wrinch, D., & Jeffreys, H. (1919). On some aspects of the theory of probability. Philosophical Magazine, 38, 715–731.Google Scholar
  795. Wrinch, D., & Jeffreys, H. (1921). On certain fundamental principles of scientific inquiry. Philosophical Magazine, 42, 369–390.Google Scholar
  796. Wrinch, D., & Jeffreys, H. (1923). On certain fundamental principles of scientific inquiry. Philosophical Magazine, 45, 368–374.Google Scholar
  797. 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
  798. Yager, R. R., & Liu, L. (Eds.). (2007). Classic works on the Dempster-Shafer theory of belief functions. Heidelberg: Springer.Google Scholar
  799. 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
  800. 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
  801. 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
  802. Yule, U. (1897). On the theory of correlation. Journal of the Royal Statistical Society, 60, 812–854.Google Scholar
  803. 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
  804. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.Google Scholar
  805. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Daniel Courgeau
    • 1
  1. 1.Institut National d’Etudes Démographiques (INED)Paris Cedex 20France

Personalised recommendations