Two noniterative algorithms for computing posteriors
Yang, Jun1,2; Zou, Guohua2; Zhao, Yu1
AbstractIn this paper, we first propose a noniterative sampling method to obtain an i.i.d. sample approximately from posteriors by combining the inverse Bayes formula, sampling/importance resampling and posterior mode estimates. We then propose a new exact algorithm to compute posteriors by improving the PMDA-Exact using the sampling-wise IBF. If the posterior mode is available from the EM algorithm, then these two algorithms compute posteriors well and eliminate the convergence problem of Markov Chain Monte Carlo methods. We show good performances of our methods by some examples.
KeywordBayesian computation data augmentation EM algorithm inverse Bayes formula sampling/importance resampling PMDA-Exact
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000257721500006
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Document Type期刊论文
Corresponding AuthorYang, Jun
Affiliation1.Beijing Univ Aeronaut & Astronaut, Dept Syst Engn Engn Technol, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Yang, Jun,Zou, Guohua,Zhao, Yu. Two noniterative algorithms for computing posteriors[J]. COMPUTATIONAL STATISTICS,2008,23(3):443-453.
APA Yang, Jun,Zou, Guohua,&Zhao, Yu.(2008).Two noniterative algorithms for computing posteriors.COMPUTATIONAL STATISTICS,23(3),443-453.
MLA Yang, Jun,et al."Two noniterative algorithms for computing posteriors".COMPUTATIONAL STATISTICS 23.3(2008):443-453.
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