Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments

作者：John F Geweke · 年份：1992 · DOI：10.1093/oso/9780198522669.003.0010 · 被引用次数：1524 · 研究领域：Statistical Methods and Bayesian Inference、Statistical Methods and Inference、Bayesian Methods and Mixture Models

Abstract Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods from spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model with informative priors, and in the Tobit censored regression model.