Abstract
The main point of the paper is to show how popular probabilistic measures of incremental confirmation and statistical relevance with qualitatively different features can be embedded smoothly in generalized parametric families. In particular, I will show that the probability difference, log probability ratio, log likelihood ratio, odds difference, so-called improbability difference, and Gaifman’s measures of confirmation can all be subsumed within a convenient biparametric continuum. One intermediate step of this project may have interest on its own, as it provides a unified representation of graded belief of which both probabilities and odds are special cases.
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- 1.
A regular probability function never assigns probability 0 to a statement unless it expresses a logical falsehood (i.e., for any α ∈ L C , P(α) > 0). Regularity can be motivated as a way to represent credences that are non-dogmatic as concerns L C (see Howson 2000, p. 70). It is known to be a convenient but not entirely innocent assumption (see Festa 1999; Kuipers 2000 for discussion; also see Pruss 2013).
- 2.
Two ordinally equivalent measures C and C* are such that for any h,k,e,f ∈ L c and any P ∈ P, C(h,e) ⋛ C(k,f) if and only if C*(h,e) ⋛ C*(k,f).
- 3.
A different way to connect and subsume probabilities and odds was already suggested by Festa (2008). Festa defined a parametric family of “belief functions” B α (x) = P(x)/[1 + αP(x)] with α ∈[−1,∞), so that B –1(x) = O(x) and B 0(x) = P(x).
References
A’Court, C., R. Stevens, and C. Haneghan. 2012. Against all odds? Improving the understanding of risk reporting. British Journal of General Practice 62: e220–e223.
Barratt, A., P.C. Wyer, R. Hatala, T. McGinn, A.L. Dans, S. Keitz, V. Moyer, and G. Guyatt. 2004. Tips for learners of evidence-based medicine, 1: Relative risk reduction, absolute risk reduction, and number needed to treat. Canadian Medical Association Journal 171: 353–358.
Brössel, P. 2013. The problem of measure sensitivity redux. Philosophy of Science 80: 378–397.
Carnap, R. 1950. Logical foundations of probability. Chicago: University of Chicago Press.
Chandler, J. 2013. Contrastive confirmation: Some competing accounts. Synthese 190: 129–138.
Cheng, P. 1997. From covariation to causation: A causal power theory. Psychological Review 104: 367–405.
Climenhaga, N. 2013. A problem for the alternative difference measure of confirmation. Philosophical Studies 164: 643–651.
Cohen, M.P. 2015. On three measures of explanatory power with axiomatic representations. British Journal for the Philosophy of Science. doi:10.1093/bjps/axv017.
Cornfield, J. 1951. A method for estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute 11: 1269–1275.
Crupi, V. 2015. Inductive logic. Journal of Philosophical Logic 44: 641–650.
Crupi, V., and K. Tentori. 2012. A second look at the logic of explanatory power (with two novel representation theorems). Philosophy of Science 79: 365–385.
Crupi, V., N. Chater, and K. Tentori. 2013. New axioms for probability and likelihood ratio measures. British Journal for the Philosophy of Science 64: 189–204.
———. 2013. Confirmation as partial entailment: A representation theorem in inductive logic. Journal of Applied Logic 11: 364–372. [Erratum in Journal of Applied Logic 12: 230–231].
———. 2014. State of the field: Measuring information and confirmation. Studies in the History an Philosophy of Science A 47: 81–90.
Crupi, V., K. Tentori, and M. Gonzalez. 2007. On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science 74: 229–252.
Crupi, V., R. Festa, and C. Buttasi. 2010. Towards a grammar of Bayesian confirmation. In Epistemology and methodology of science, ed. M. Suárez, M. Dorato, and M. Rédei, 73–93. Dordrecht: Springer.
Eells, E. 1991. Probabilistic causality. Cambridge: Cambridge University Press.
Festa, R. 1999. Bayesian confirmation. In Experience, reality, and scientific explanation, ed. M. Galavotti and A. Pagnini, 55–87. Dordrecht: Kluwer.
———. 2008. On the Matthew effect and other properties of Bayesian confirmation [talk]. Workshop on probability, confirmation, and fallacies, University of Leuven, April 6, 2008.
———. 2012. “For unto every one that hath shall be given”: Matthew properties for incremental confirmation. Synthese 184: 89–100.
Festa, R., and G. Cevolani 2016. Unfolding the grammar of Bayesian confirmation: Likelihood and anti-likelihood principles. Philosophy of Science, forthcoming.
Fitelson, B. 2001. A Bayesian account of independent evidence with applications. Philosophy of Science 68: S123–S140.
———. 2007. Likelihoodism, Bayesianism, and relational confirmation. Synthese 156: 473–489.
Fitelson, B., and C. Hitchcock. 2011. Probabilistic measures of causal strength. In Causality in the sciences, ed. P. McKay Illari, F. Russo, and J. Williamson, 600–627. Oxford: Oxford University Press.
Gaifman, H. 1979. Subjective probability, natural predicates, and Hempel’s ravens. Erkenntnis 14: 105–147.
Garson, G.D. 2012. Measures of association. Asheboro: Statistical Associates Publishers.
Glass, D.H. 2013. Confirmation measures of association rule interestingness. Knowledge-Based Systems 44: 65–77.
Good, I.J. 1950. Probability and the weighing of evidence. London: Griffin.
———. 1961. A causal calculus I. British Journal for the Philosophy of Science 11: 305–318.
———. 1962. A causal calculus II. British Journal for the Philosophy of Science 12: 43–51.
Hájek, A., and J. Joyce. 2008. Confirmation. In Routledge companion to the philosophy of science, ed. S. Psillos and M. Curd, 115–129. New York: Routledge.
Havrda, J., and F. Charvát. 1967. Quantification method of classification processes: Concept of structural a-entropy. Kybernetica 3: 30–35.
Heckerman, D. 1988. An axiomatic framework for belief updates. In Uncertainty in artificial intelligence 2, ed. J.F. Lemmer and L.N. Kanal, 11–22. Amsterdam: North-Holland.
Howson, C. 2000. Hume’s problem: Induction and the justification of belief. Oxford: Oxford University Press.
Iranzo, V., and I. Martínez de Lejarza. 2012. On ratio measures of confirmation. Journal for General Philosophy of Science 44: 193–200.
Joyce, J. 2004. Bayes’s theorem. In ed. E.N. Zalta, The Stanford encyclopedia of philosophy (Summer 2004 Edition). URL:http://plato.stanford.edu/archives/sum2004/entries/bayes-theorem/
Keylock, J.C. 2005. Simpson diversity and the Shannon-Wiener index as special cases of a generalized entropy. Oikos 109: 203–207.
Kuipers, T. 2000. From instrumentalism to constructive realism. Dordrecht: Reidel.
Lewis, D. 1986. Postscripts to ‘causation’. In Philosophical papers, vol. II, 173–213. Oxford: Oxford University Press.
Milne, P. 1996. Log[P(h|eb)/P(h|b)] is the one true measure of confirmation. Philosophy of Science 63: 21–26.
———. 2012. On measures of confirmation. Manuscript.
Park, I. 2014. Confirmation measures and collaborative belief updating. Synthese 191: 3955–3975.
Pruss, A.R. 2013. Probability, regularity, and cardinality. Philosophy of Science 80: 231–240.
Rips, L. 2001. Two kinds of reasoning. Psychological Science 12: 129–134.
Roche, W. 2014. A note on confirmation and Matthew properties. Logic & Philosophy of Science 12: 91–101.
Roche, W., and T. Shogenji. 2014. Dwindling confirmation. Philosophy of Science 81: 114–137.
Royall, R. 1997. Statistical evidence: A likelihood paradigm. London: Chapman & Hall.
Schippers, M., and M. Siebel. 2015. Inconsistency as a touchstone for coherence measures. Theoria 30: 11–41.
Schupbach, J.N., and J. Sprenger. 2011. The logic of explanatory power. Philosophy of Science 78: 105–127.
Sober, E. 1990. Contrastive empiricism. In Minnesota studies in the philosophy of science: Scientific theories, vol. 14, ed. C.W. Savage, 392–412. Minneapolis: University of Minnesota Press.
Sprenger, J. 2016a. Two impossibility results for measures of corroboration. British Journal for the Philosophy of Science, forthcoming.
———. 2016b. Foundations for a probabilistic theory of causal strength. See: http://philsci-archive.pitt.edu/11927/1/GradedCausation-v2.pdf.
Tsallis, C. 1988. Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics 52: 479–487.
Yule, G.U. 1900. On the association of attributes in statistics, with illustrations from the material from the childhood society. Philosophical Transactions A 194: 257–319.
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Crupi, V. (2017). Generalized Confirmation and Relevance Measures. In: Massimi, M., Romeijn, JW., Schurz, G. (eds) EPSA15 Selected Papers. European Studies in Philosophy of Science, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-53730-6_23
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