Model Checking After Bayesian Inference

Matteo Pozzi () and Daniele Zonta ()
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Matteo Pozzi: Carnegie Mellon University
Daniele Zonta: University of Strathclyde

A chapter in Risk and Reliability Analysis: Theory and Applications, 2017, pp 317-339 from Springer

Abstract: Abstract Bayesian analysis provides a consistent logical framework for processing data, inferring parameters and estimating relevant quantities in engineering problems. However, its outcomes are valid conditional to the specific model assumptions. Whether these assumptions are questioned, possibly because of some factors knowingly left out, they can be checked by further analysis of the available empirical data. Again, this can be done inside the Bayesian framework, by probabilistically comparing expanded models with the original one; however, this may be computational impractical in many applications. Test statistics and p-value analysis, historically developed under the frequentist approach but adapted to the Bayesian setting, provide an alternative for model checking coupled with probabilistic inference. In this chapter, we illustrate the relation between p-value analysis and Bayesian model comparison: after presenting it in a general context, we focus on Gaussian linear models under known perturbation, for which this relation can be stated in close formulas, and explore an example outside that domain.

Keywords: Posterior Probability; Model Check; Bayesian Model; Candidate Model; Predictive Distribution (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-319-52425-2_14

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DOI: 10.1007/978-3-319-52425-2_14

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