Efficient Bayes factor estimation from the reversible jump output

• 成果类型: Article
• 署名作者: Bartolucci, F; Scaccia, L; Mira, A
• 署名单位: University of Perugia; University of Macerata; University of Insubria
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.1.41
• 发表日期: 2006
• 页码: 4152
• 关键词: monte-carlo methods; normalizing constants; marginal likelihood; estimating ratios; MODEL; probability

摘要

We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green (1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump algorithm for jumping between models. The proposed estimators are very easily computed and lead to a substantial gain of efficiency in estimating the Bayes factor over the standard estimator based on the reversible jump output. This is illustrated through a series of Monte Carlo simulations involving a linear and a logistic regression model.