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


  • Hedibert F. Lopes

    () (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

  • Justin L. Tobias

    () (Economics Department, Purdue University, Lafayette, Indiana 47907)


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

  • Hedibert F. Lopes & Justin L. Tobias, 2011. "Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 107-131, September.
  • Handle: RePEc:anr:reveco:v:3:y:2011:p:107-131

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    References listed on IDEAS

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    More about this item


    Bayesian methods; marginal likelihood; scale mixture of normals; Dirichlet process mixture; factor models; Markov chain Monte Carlo; Gibbs sampler; sequential Monte Carlo;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection


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