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

Hedibert F. Lopes () and Justin Tobias ()
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Hedibert F. Lopes: Booth School of Business, University of Chicago, Chicago, Illinois 60637

Annual Review of Economics, 2011, vol. 3, issue 1, 107-131

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.

Keywords: Bayesian methods; marginal likelihood; scale mixture of normals; Dirichlet process mixture; factor models; Markov chain Monte Carlo; Gibbs sampler; sequential Monte Carlo (search for similar items in EconPapers)
JEL-codes: C01 C11 C52 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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