Skip to main content

Generalized Confirmation and Relevance Measures

  • Conference paper
  • First Online:

Part of the book series: European Studies in Philosophy of Science ((ESPS,volume 5))

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.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    A regular probability function never assigns probability 0 to a statement unless it expresses a logical falsehood (i.e., for any αL 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. 2.

    Two ordinally equivalent measures C and C* are such that for any h,k,e,fL c and any PP, C(h,e) ⋛ C(k,f) if and only if C*(h,e) ⋛ C*(k,f).

  3. 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Brössel, P. 2013. The problem of measure sensitivity redux. Philosophy of Science 80: 378–397.

    Article  Google Scholar 

  • Carnap, R. 1950. Logical foundations of probability. Chicago: University of Chicago Press.

    Google Scholar 

  • Chandler, J. 2013. Contrastive confirmation: Some competing accounts. Synthese 190: 129–138.

    Article  Google Scholar 

  • Cheng, P. 1997. From covariation to causation: A causal power theory. Psychological Review 104: 367–405.

    Article  Google Scholar 

  • Climenhaga, N. 2013. A problem for the alternative difference measure of confirmation. Philosophical Studies 164: 643–651.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Crupi, V. 2015. Inductive logic. Journal of Philosophical Logic 44: 641–650.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • ———. 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].

    Google Scholar 

  • ———. 2014. State of the field: Measuring information and confirmation. Studies in the History an Philosophy of Science A 47: 81–90.

    Article  Google Scholar 

  • Crupi, V., K. Tentori, and M. Gonzalez. 2007. On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science 74: 229–252.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Eells, E. 1991. Probabilistic causality. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Festa, R. 1999. Bayesian confirmation. In Experience, reality, and scientific explanation, ed. M. Galavotti and A. Pagnini, 55–87. Dordrecht: Kluwer.

    Chapter  Google Scholar 

  • ———. 2008. On the Matthew effect and other properties of Bayesian confirmation [talk]. Workshop on probability, confirmation, and fallacies, University of Leuven, April 6, 2008.

    Google Scholar 

  • ———. 2012. “For unto every one that hath shall be given”: Matthew properties for incremental confirmation. Synthese 184: 89–100.

    Article  Google Scholar 

  • Festa, R., and G. Cevolani 2016. Unfolding the grammar of Bayesian confirmation: Likelihood and anti-likelihood principles. Philosophy of Science, forthcoming.

    Google Scholar 

  • Fitelson, B. 2001. A Bayesian account of independent evidence with applications. Philosophy of Science 68: S123–S140.

    Article  Google Scholar 

  • ———. 2007. Likelihoodism, Bayesianism, and relational confirmation. Synthese 156: 473–489.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Gaifman, H. 1979. Subjective probability, natural predicates, and Hempel’s ravens. Erkenntnis 14: 105–147.

    Google Scholar 

  • Garson, G.D. 2012. Measures of association. Asheboro: Statistical Associates Publishers.

    Google Scholar 

  • Glass, D.H. 2013. Confirmation measures of association rule interestingness. Knowledge-Based Systems 44: 65–77.

    Article  Google Scholar 

  • Good, I.J. 1950. Probability and the weighing of evidence. London: Griffin.

    Google Scholar 

  • ———. 1961. A causal calculus I. British Journal for the Philosophy of Science 11: 305–318.

    Article  Google Scholar 

  • ———. 1962. A causal calculus II. British Journal for the Philosophy of Science 12: 43–51.

    Google Scholar 

  • 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.

    Google Scholar 

  • Havrda, J., and F. Charvát. 1967. Quantification method of classification processes: Concept of structural a-entropy. Kybernetica 3: 30–35.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Howson, C. 2000. Hume’s problem: Induction and the justification of belief. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Iranzo, V., and I. Martínez de Lejarza. 2012. On ratio measures of confirmation. Journal for General Philosophy of Science 44: 193–200.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kuipers, T. 2000. From instrumentalism to constructive realism. Dordrecht: Reidel.

    Book  Google Scholar 

  • Lewis, D. 1986. Postscripts to ‘causation’. In Philosophical papers, vol. II, 173–213. Oxford: Oxford University Press.

    Google Scholar 

  • Milne, P. 1996. Log[P(h|eb)/P(h|b)] is the one true measure of confirmation. Philosophy of Science 63: 21–26.

    Article  Google Scholar 

  • ———. 2012. On measures of confirmation. Manuscript.

    Google Scholar 

  • Park, I. 2014. Confirmation measures and collaborative belief updating. Synthese 191: 3955–3975.

    Article  Google Scholar 

  • Pruss, A.R. 2013. Probability, regularity, and cardinality. Philosophy of Science 80: 231–240.

    Article  Google Scholar 

  • Rips, L. 2001. Two kinds of reasoning. Psychological Science 12: 129–134.

    Article  Google Scholar 

  • Roche, W. 2014. A note on confirmation and Matthew properties. Logic & Philosophy of Science 12: 91–101.

    Google Scholar 

  • Roche, W., and T. Shogenji. 2014. Dwindling confirmation. Philosophy of Science 81: 114–137.

    Article  Google Scholar 

  • Royall, R. 1997. Statistical evidence: A likelihood paradigm. London: Chapman & Hall.

    Google Scholar 

  • Schippers, M., and M. Siebel. 2015. Inconsistency as a touchstone for coherence measures. Theoria 30: 11–41.

    Article  Google Scholar 

  • Schupbach, J.N., and J. Sprenger. 2011. The logic of explanatory power. Philosophy of Science 78: 105–127.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Sprenger, J. 2016a. Two impossibility results for measures of corroboration. British Journal for the Philosophy of Science, forthcoming.

    Google Scholar 

  • ———. 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vincenzo Crupi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

Publish with us

Policies and ethics