标题： 基于Jeffreys散度先验的贝叶斯假设检验推广 显示英文标题

标题： Generalization of Jeffreys' divergence based priors for Bayesian hypothesis testing

摘要： 本文中我们引入了基于竞争模型间发散度量的目标恰当先验分布，用于假设检验和模型选择；我们称它们为基于发散的（DB）先验。 DB先验具有简单形式和良好的性质，如信息（有限样本）一致性；通常，它们与其它现有提议（如内在先验）相似；此外，在正态线性模型情况下，它们恰好还原为Jeffreys-Zellner-Siow先验。 更重要的是，在非正则模型和混合模型等具有挑战性的场景下，DB先验定义良好且非常合理，而替代提议则不然。 我们推导出DB先验的近似值以及相关的贝叶斯因子的MCMC和渐近表达式。 显示英文摘要

摘要： In this paper we introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence based (DB) priors. DB priors have simple forms and desirable properties, like information (finite sample) consistency; often, they are similar to other existing proposals like the intrinsic priors; moreover, in normal linear models scenarios, they exactly reproduce Jeffreys-Zellner-Siow priors. Most importantly, in challenging scenarios such as irregular models and mixture models, the DB priors are well defined and very reasonable, while alternative proposals are not. We derive approximations to the DB priors as well as MCMC and asymptotic expressions for the associated Bayes factors.