文章基本信息

标题：On some difficulties with a posterior probability approximation technique,
本地全文：下载
作者：Christian P. Robert ; Jean-Michel Marin
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2008
卷号：03
期号：02
页码：427-442
DOI：10.1214/08-BA316
出版社：International Society for Bayesian Analysis
摘要：In Scott (2002) and Congdon (2006), a new method is advanced to compute posterior probabilities of models under consideration. It is based solely on MCMC outputs restricted to single models, i.e., it is bypassing reversible jump and other model exploration techniques. While it is indeed possible to approxi- mate posterior probabilities based solely on MCMC outputs from single models, as demonstrated by Gelfand and Dey (1994) and Bartolucci et al. (2006), we show that the proposals of Scott (2002) and Congdon (2006) are biased and advance sev- eral arguments towards this thesis, the primary one being the confusion between model-based posteriors and joint pseudo-posteriors. From a practical point of view, the bias in Scott's (2002) approximation appears to be much more severe than the one in Congdon's (2006), the latter being often of the same magnitude as the posterior probability it approximates, although we also exhibit an example where the divergence from the true posterior probability is extreme.
关键词：Bayesian model choice, posterior approximation, reversible jump, Markov Chain Monte Carlo (MCMC), pseudo-priors, unbiasedness, improperty.