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Archive for the ‘model checking’ Category

Recent publications by David Bickel

1 May 2019 Leave a comment

Recent preprints by David Bickel

1 April 2019 Leave a comment

Fiducial model averaging of Bayesian models and of frequentist models

1 January 2019 Leave a comment

D. R. Bickel, “A note on fiducial model averaging as an alternative to checking Bayesian and frequentist models,” Communications in Statistics – Theory and Methods 47, 3125-3137 (2018). Full article2015 preprint

Pre-data insights update priors via Bayes’s theorem

1 September 2018 Leave a comment

An idealized Cromwell’s rule

1 June 2018 Leave a comment

Cromwell’s principle idealized under the theory of large deviations

Seminar, Statistics and Probability Research Group, University of Ottawa

Ottawa, Ontario

April 27, 2018

David R. Bickel

University of Ottawa

Abstract. Cromwell’s principle requires that the prior probability that one’s assumptions are incorrect is greater than 0. That is relevant to Bayesian model checking since diagnostics often reveal that prior distributions require revision, which would be impossible under Bayes’s theorem if those priors were 100% probable. The idealized Cromwell’s principle makes the probability of making incorrect assumptions arbitrarily small. Enforcing that principle under large deviations theory leads to revising Bayesian models by maximum entropy in wide generality.

How to make decisions using somewhat reliable posterior distributions

15 January 2018 Leave a comment
Categories: model checking, preprints

Do models have probabilities or just possibilities?

15 January 2018 Leave a comment

Andrew says: David:I don’t think it makes sense to talk of the probability of a model. See this paper with Shalizi for much discussion of this point.

David Bickel says: If models do not have probabilities, perhaps they have possibilities in the sense of possibility theory. For example, the possibility of a model might be a function of its adequacy according to a model checking procedure: Appendix B of https://goo.gl/5s7bS3

Source: Not everyone’s aware of falsificationist Bayes – Statistical Modeling, Causal Inference, and Social Science