Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis

Posted: 31 Aug 2011

See all articles by Hedibert F. Lopes

Hedibert F. Lopes

University of Chicago - Booth School of Business

Justin L. Tobias

University of California, Irvine - Department of Economics

Date Written: September 2011

Abstract

In this review we explore issues of the sensitivity of Bayes estimates to the prior and form of the likelihood. With respect to the prior, we argue that non-Bayesian analyses also incorporate prior information, illustrate that the Bayes posterior mean and the frequentist maximum likelihood estimator are often asymptotically equivalent, review a simple computational strategy for analyzing sensitivity to the prior in practice, and finally document the potentially important role of the prior in Bayesian model comparison. With respect to issues of likelihood robustness, we review a variety of computational strategies for significantly expanding the maintained sampling model, including the use of finite Gaussian mixture models and models based on Dirichlet process priors.

Suggested Citation

Lopes, Hedibert F. and Tobias, Justin L., Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis (September 2011). Annual Review of Economics, Vol. 3, pp. 107-131, 2011. Available at SSRN: https://ssrn.com/abstract=1920115 or http://dx.doi.org/10.1146/annurev-economics-111809-125134

Hedibert F. Lopes (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Justin L. Tobias

University of California, Irvine - Department of Economics

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

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