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贝叶斯模型选择中偏差信息准则的理论依据与应用AI 解读

This paper provides a theoretical justification for the Deviance Information Criterion (DIC) as a Bayesian model selection tool using MCMC output. Unlike Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance model adequacy against complexity without considering prior information, DIC incorporates priors into this trade-off. The contributions of this paper are two-fold. First, it demonstrates that when a plug-in predictive distribution —…

通过幂后验与重要性抽样改进边际似然估计AI 解读

Power posteriors have become popular in estimating the marginal likelihood of a Bayesian model. A power posterior is referred to as the posterior distribution that is proportional to the likelihood raised to a power b∈[0,1]. Important power-posterior-based algorithms include thermodynamic integration (TI) of Friel and Pettitt (2008) and steppingstone sampling (SS) of Xie et al. (2011). In this paper, it is shown that the Bernstein–von Mises (BvM) theorem holds for power…