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Partition Weighted Approach For Estimating the Marginal Posterior Density With Applications

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posted on 25.10.2019, 13:15 by Yu-Bo Wang, Ming-Hui Chen, Lynn Kuo, Paul O. Lewis

The computation of marginal posterior density in Bayesian analysis is essential in that it can provide complete information about parameters of interest. Furthermore, the marginal posterior density can be used for computing Bayes factors, posterior model probabilities, and diagnostic measures. The conditional marginal density estimator (CMDE) is theoretically the best for marginal density estimation but requires the closed-form expression of the conditional posterior density, which is often not available in many applications. We develop the partition weighted marginal density estimator (PWMDE) to realize the CMDE. This unbiased estimator requires only a single Markov chain Monte Carlo output from the joint posterior distribution and the known unnormalized posterior density. The theoretical properties and various applications of the PWMDE are examined in detail. The PWMDE method is also extended to the estimation of conditional posterior densities. We carry out simulation studies to investigate the empirical performance of the PWMDE and further demonstrate the desirable features of the proposed method with two real data sets from a study of dissociative identity disorder patients and a prostate cancer study, respectively. Supplementary materials for this article are available online.


This material is based upon work supported by the National Science Foundation under Grant No. DEB-1354146. Dr. M.-H. Chen’s research was also partially supported by NIH grants #GM70335 and #P01CA142538.