Home
> empirical Bayes, imprecise probability, maximum entropy, model checking, possibility theory, publications, simply explained, slides, statistical evidence > Inference after checking the prior & sampling model

## Inference after checking the prior & sampling model

1 September 2015
DRB

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 version | 2014 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:

- 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.
- 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.

*Related*

### Follow me on Twitter

My Tweets### New content

- R package for estimating local false discovery rates using empirical Bayes methods
- Lower the statistical significance threshold to 0.005—or 0.001?
- “The Fiducialist Papers” archived in favor of “sIBEe”
- Pre-data insights update priors via Bayes’s theorem
- How to adjust statistical inferences for the simplicity of distributions

### More content

### Archives

### Emphases

applications welcome complexity empirical Bayes fiducial inference gene expression genetic association imprecise probability maximum entropy MDL metabolomics model checking ontology parameter restriction possibility theory proteomics publications reviews simply explained slides software statistical evidence trainee author

### Slideshow

This slideshow requires JavaScript.