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Archive for the ‘maximum entropy’ Category

Recent publications by David Bickel

1 May 2019 Leave a comment

Recent preprints by David Bickel

1 April 2019 Leave a comment

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.

An R package to transform false discovery rates to posterior probability estimates

1 May 2018 Leave a comment

There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator.

Source: CRAN – Package CorrectedFDR

Inference after eliminating Bayesian models of excessive codelength

1 November 2017 Leave a comment

Inference after eliminating Bayesian models of insufficient evidence

1 December 2016 Leave a comment

A Bayesian approach to informing decision makers

23 September 2016 Leave a comment

Estimates of the local false discovery rate based on prior information: Application to GWAS

1 August 2016 Leave a comment

False discovery rates are misleadingly low

2 March 2016 Leave a comment