Hostname: page-component-7479d7b7d-jwnkl Total loading time: 0 Render date: 2024-07-11T11:07:39.408Z Has data issue: false hasContentIssue false

Divisive Conditioning: Further Results on Dilation

Published online by Cambridge University Press:  01 April 2022

Timothy Herron*
Affiliation:
Department of Philosophy, Carnegie Mellon University
Teddy Seidenfeld*
Affiliation:
Departments of Philosophy and Statistics, Carnegie Mellon University
Larry Wasserman*
Affiliation:
Department of Statistics, Carnegie Mellon University
*
Send reprint requests to the second or third author, Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890.

Abstract

Conditioning can make imprecise probabilities uniformly more imprecise. We call this effect “dilation”. In a previous paper (1993), Seidenfeld and Wasserman established some basic results about dilation. In this paper we further investigate dilation on several models. In particular, we consider conditions under which dilation persists under marginalization and we quantify the degree of dilation. We also show that dilation manifests itself asymptotically in certain robust Bayesian models and we characterize the rate at which dilation occurs.

Type
Research Article
Copyright
Copyright © Philosophy of Science Association 1997

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The first two authors were supported by NSF Grant SES-9208942. The third author was supported by NSF Grants DMS-9005858 and DMS-9357646 and NIH Grant RO1-CA54852-01.

References

Berger, J. (1984), “The robust Bayesian viewpoint” (with discussion), in J. Kadane (ed.), Robustness in Bayesian Statistics. Amsterdam: North-Holland, pp. 63124.Google Scholar
Berger, J. (1985), Statistical Decision Theory (2nd Edition). New York: Springer-Verlag.Google Scholar
Berger, J. (1990), “Robust Bayesian analysis: sensitivity to the prior”, J. Statist. Plann. Inference 25: 303328.10.1016/0378-3758(90)90079-ACrossRefGoogle Scholar
Berger, J. and Berliner, M. (1986), “Robust Bayes and empirical Bayes analysis with epsilon-contaminated priors”, Ann Statist. 14: 461486.10.1214/aos/1176349933CrossRefGoogle Scholar
Blackwell, D. and Dubins, L. (1962), “Merging of opinions with increasing information”, Ann. Statist. 33: 882887.10.1214/aoms/1177704456CrossRefGoogle Scholar
Dall'Aglio, G. (1972), “Frechet Classes and Compatibility of Distribution Functions”, Symposia Mathematica Symposia Mathematica: 9131. Providence, RI: Amer. Math. Soc.Google Scholar
DeRobertis, L. and Hartigan, J. A. (1981), “Bayesian inference using intervals of measures”, Ann. Statist. 9: 235244.Google Scholar
Fine, T. L. (1988), “Lower probability models for uncertainty and nondeterministic processes”, J. Stat. Planning and Inference 20: 389411.10.1016/0378-3758(88)90099-7CrossRefGoogle Scholar
Good, I. J. (1952), “Rational decisions”, J. Roy. Statist. Soc. B. 14: 107114.Google Scholar
Huber, P. J. (1981), Robust Statistics. New York: Wiley.10.1002/0471725250CrossRefGoogle Scholar
Huber, P. J. and Strassen, V. (1973), “Minimax tests and the Neyman-Pearson lemma for capacities”, Ann. Stat. 1: 251263.10.1214/aos/1176342363CrossRefGoogle Scholar
Herron, T., Seidenfeld, T., and Wasserman, L. (1994), “The extent of dilation of sets of probabilities and the asymptotics of robust Bayesian inference”, in Hull, D., Forbes, M., and Burian, R. M. (eds.), PSA-1994. vol. 2. East Lansing: PSA, pp. 250259.Google Scholar
Jeffreys, H. (1961), Theory of Probability. Oxford: Clarendon Press.Google Scholar
Kyburg, H. (1961), Probability and the Logic of Rational Belief. Middleton, CT: Wesleyan University Press.Google Scholar
Kyburg, H. (1974), The Logical Foundations of Statistical Inference. Dordrecht: Reidel.10.1007/978-94-010-2175-3CrossRefGoogle Scholar
Lavine, M. (1991), “Sensitivity in Bayesian statistics: the prior and the likelihood,” J. Amer. Statist. Assoc. 86: 396399.10.1080/01621459.1991.10475055CrossRefGoogle Scholar
Lavine, M., Wasserman, L., and Wolpert, R. (1993), “Linearization of Bayesian robustness problems”, J. Statist. Plann. and Inference 37: 307316.Google Scholar
Levi, I. (1974), “On indeterminate probabilities”, J. Phil. 71: 391418.10.2307/2025161CrossRefGoogle Scholar
Levi, I. (1980), The enterprise of knowledge. Cambridge, MA: MIT Press.Google Scholar
Levi, I. (1982), “Conflict and social agency”, J. Phil. 79: 231247.10.2307/2026060CrossRefGoogle Scholar
Pericchi, L. R. and Walley, P. (1991), “Robust Bayesian credible intervals and prior ignorance”, Internat. Statist. Review 59: 124.10.2307/1403571CrossRefGoogle Scholar
Rachev, S. T. (1985), “The Monge-Kantorovich mass transference problem and its stochastic applications”, Theory of Probability and Its Applications 29: 647671.10.1137/1129093CrossRefGoogle Scholar
Savage, L. J. (1972), The Foundations of Statistics, (2nd Edition). New York: Dover.Google Scholar
Schervish, M. and Seidenfeld, T. (1990), “An approach to consensus and certainty with increasing shared evidence”, J. Stat. Planning and Inference 25: 401414.10.1016/0378-3758(90)90084-8CrossRefGoogle Scholar
Seidenfeld, T., Kadane, J., and Schervish, M. (1989), “On the shared preferences of two Bayesian decision makers”, J. Phil. 86: 225244.10.2307/2027108CrossRefGoogle Scholar
Seidenfeld, T., Schervish, M., and Kadane, J. (1995), “A representation of partially ordered preferences”, Ann. Statist. 23: 21682217.10.1214/aos/1034713653CrossRefGoogle Scholar
Seidenfeld, T. and Wasserman, L. (1993), “Dilation for convex sets of probabilities”, Ann. Statist. 21: 11391154.10.1214/aos/1176349254CrossRefGoogle Scholar
Smith, C. A. B. (1961), “Consistency in statistical inference and decision”, J. Roy. Statist. Soc. B. 23: 125.Google Scholar
Walley, P. (1991), Statistical Reasoning With Imprecise Probabilities. London: Chapman and Hall (Monographs on Statistics and Applied Probability).10.1007/978-1-4899-3472-7CrossRefGoogle Scholar
Walley, P. (1996), “Inference from multinomial data: learning from a bag of marbles” (with discussion), J. Roy. Statist. Soc. B. 58: 358.Google Scholar
Walley, P. and Fine, T. L. (1982), “Towards a frequentist theory of upper and lower probabilities”, Ann. Statist. 10: 741761.10.1214/aos/1176345868CrossRefGoogle Scholar
Wasserman, L. A. and Kadane, J. (1992), “Symmetric upper probabilities”, Ann. Statist. 20: 17201736.10.1214/aos/1176348887CrossRefGoogle Scholar