國家衛生研究院 NHRI:Item 3990099045/1865
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    题名: Bayesian marginal inference via candidate's formula
    作者: Hsiao, CK;Huang, SY;Chang, CW
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: Computing marginal probabilities is an important and fundamental issue in Bayesian inference. We present a simple method which arises from a likelihood identity for computation. The likelihood identity, called Candidate's formula, sets the marginal probability as a ratio of the prior likelihood to the posterior density. Based on Markov chain Monte Carlo output simulated from the posterior distribution, a nonparametric kernel estimate is used to estimate the posterior density contained in that ratio. This derived nonparametric Candidate's estimate requires only one evaluation of the posterior density estimate at a point. The optimal point for such evaluation can be chosen to minimize the expected mean square relative error. The results show that the best point is not necessarily the posterior mode, but rather a point compromising between high density and low Hessian. For high dimensional problems, we introduce a variance reduction approach to ease the tension caused by data sparseness. A simulation study is presented.
    关键词: Computer Science, Theory & Methods;Statistics & Probability
    日期: 2004-01
    關聯: Statistics and Computing. 2004 Jan;14(1):59-66.
    Link to: http://dx.doi.org/10.1023/B:STCO.0000009416.78796.78
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0960-3174&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000187388700006
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=3843051261
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