Evidence and Bayes Factor Estimation for Gibbs Random Fields

作     者：Friel, Nial 

作者机构：Univ Coll Dublin CASL Sch Math Sci Dublin 4 Ireland Univ Coll Dublin CASL Clique Res Cluster Dublin 4 Ireland 

出 版 物：《JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS》 (计算与图解统计学杂志)

年 卷 期：2013年第22卷第3期

页      面：518-532页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Science Foundation Ireland Research Frontiers Program [09/RFP/MTH2199] Science Foundation Ireland (SFI) [09/RFP/MTH2199] Funding Source: Science Foundation Ireland (SFI) 

主　　题：Exchange algorithm Ising model Population MCMC 

摘      要：Gibbs random fields play an important role in statistics. However, they are complicated to work with due to an intractability of the likelihood function and there has been much work devoted to finding computational algorithms to allow Bayesian inference to be conducted for such so-called doubly intractable distributions. This article extends this work and addresses the issue of estimating the evidence and Bayes factor for such models. The approach that we develop is shown to yield good performance. Supplementary materials for this article are available online.