Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis
这篇综述探讨了贝叶斯估计对先验分布和似然函数形式的敏感性，指出非贝叶斯分析也隐含先验信息，并介绍了评估先验敏感性及扩展抽样模型的计算策略，对从事贝叶斯建模的经济学者有参考价值。
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.