|Title||Partition Weighted Approach for Estimating the Marginal Posterior Density with Applications.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Wang, Yu-Bo, Ming-Hui Chen, Lynn Kuo, and Paul O. Lewis|
|Journal||J Comput Graph Stat|
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 various applications of the 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.
|Alternate Journal||J Comput Graph Stat|
|Original Publication||Partition weighted approach for estimating the marginal posterior density with applications.|
|PubMed Central ID||PMC6602590|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
Partition Weighted Approach for Estimating the Marginal Posterior Density with Applications.