Open Access
December 2018 A General Method for Robust Bayesian Modeling
Chong Wang, David M. Blei
Bayesian Anal. 13(4): 1163-1191 (December 2018). DOI: 10.1214/17-BA1090


Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic computational strategy for it. We use our method to study robust variants of several models, including linear regression, Poisson regression, logistic regression, and probabilistic topic models. We discuss the connections between our methods and existing approaches, especially empirical Bayes and James–Stein estimation.


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Chong Wang. David M. Blei. "A General Method for Robust Bayesian Modeling." Bayesian Anal. 13 (4) 1163 - 1191, December 2018.


Published: December 2018
First available in Project Euclid: 3 January 2018

zbMATH: 06989980
MathSciNet: MR3855367
Digital Object Identifier: 10.1214/17-BA1090

Keywords: Empirical Bayes , expectation-maximization , generalized linear models , probabilistic models , robust statistics , topic models , variational inference

Vol.13 • No. 4 • December 2018
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