Partition Weighted Approach for Estimating the Marginal Posterior Density with Applications.

ABSTRACT: 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 MCMC output from the joint posterior distribution and the known unnormalized posterior density. The theoretical properties and vari

SUBMITTER: Wang YB 

PROVIDER: S-EPMC6602590 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature