To refer to this page use:
|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.|
|Citation:||Wang, Chong, and David M. Blei. "A General Method for Robust Bayesian Modeling." Bayesian Analysis 13, no. 4 (2018): pp. 1163-1191. doi:10.1214/17-BA1090|
|Pages:||1163 - 1191|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Bayesian Analysis|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.