Archive for the ‘maximum entropy’ Category

Inference after eliminating Bayesian models of excessive codelength

1 November 2017 Comments off

“The maximum-entropy and minimax redundancy distribution classes of sufficiently small codelength”

10th Workshop on Information Theoretic Methods in Science and Engineering

Paris, France

September 11, 2017

David R. Bickel

University of Ottawa

Inference after eliminating Bayesian models of insufficient evidence

1 December 2016 Comments off

“Inference under the entropy-maximizing Bayesian model of sufficient evidence”

The Third International Conference on Mathematical and Computational Medicine

Columbus, Ohio

David R. Bickel

18 May 2016

A Bayesian approach to informing decision makers

23 September 2016 Comments off

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

1 August 2016 Comments off

False discovery rates are misleadingly low

2 March 2016 Comments off

D. R. Bickel, “Correcting false discovery rates for their bias toward false positives,” Working Paper, University of Ottawa, deposited in uO Research at (2016). 2016 preprint | Slides: CFDR and RFDR for SSC 2017

12 June 2017: URL updated and slides added


Coherent inference after checking a prior

7 January 2016 Comments off

Inference after checking the prior & sampling model

1 September 2015 Comments off

D. R. Bickel, “Inference after checking multiple Bayesian models for data conflict and applications to mitigating the influence of rejected priors,” International Journal of Approximate Reasoning 66, 53–72 (2015). Simple explanation | Published version2014 preprint | Slides


The proposed procedure combines Bayesian model checking with robust Bayes acts to guide inference whether or not the model is found to be inadequate:

  1. The first stage of the procedure checks each model within a large class of models to determine which models are in conflict with the data and which are adequate for purposes of data analysis.
  2. The second stage of the procedure applies distribution combination or decision rules developed for imprecise probability.

This proposed procedure is illustrated by the application of a class of hierarchical models to a simple data set.

The link Simple explanation was added on 6 June 2017.