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• W2194761908 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 strategy for computing with 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." @default.
• W2194761908 created "2016-06-24" @default.
• W2194761908 creator A5007803202 @default.
• W2194761908 creator A5070920982 @default.
• W2194761908 date "2015-10-17" @default.
• W2194761908 modified "2023-09-27" @default.
• W2194761908 title "A General Method for Robust Bayesian Modeling" @default.
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• W2194761908 doi "https://doi.org/10.48550/arxiv.1510.05078" @default.
• W2194761908 hasPublicationYear "2015" @default.
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