The Intrinsic Bayes Factor for Model Selection and Prediction
提出内在贝叶斯因子，仅需无信息先验即可自动计算，用于嵌套或非嵌套模型的比较与预测，解决了传统贝叶斯因子依赖主观先验的问题。
Abstract In the Bayesian approach to model selection or hypothesis testing with models or hypotheses of differing dimensions, it is typically not possible to utilize standard noninformative (or default) prior distributions. This has led Bayesians to use conventional proper prior distributions or crude approximations to Bayes factors. In this article we introduce a new criterion called the intrinsic Bayes factor, which is fully automatic in the sense of requiring only standard noninformative priors for its computation and yet seems to correspond to very reasonable actual Bayes factors. The criterion can be used for nested or nonnested models and for multiple model comparison and prediction. From another perspective, the development suggests a general definition of a “reference prior” for model comparison.