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- Title
A General Method for Robust Bayesian Modeling.
- Authors
Chong Wang; Blei, David M.
- Abstract
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.
- Subjects
REGRESSION analysis; POISSON regression; NATURAL language processing; SOCIAL sciences; GIBBS sampling
- Publication
Bayesian Analysis, 2018, Vol 13, Issue 4, p1163
- ISSN
1936-0975
- Publication type
Article
- DOI
10.1214/17-BA1090