Multiple Bayes Factors for Testing Hypotheses
本文引入部分和多重贝叶斯因子，用于在参数空间划分的统计实验中对假设进行两两比较，并通过引入合适的先验分布类进行稳健贝叶斯分析，计算贝叶斯因子和后验概率的上下界。
Abstract Partial and multiple Bayes factors are introduced to obtain pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. Robust Bayesian analyses are performed by introducing suitable classes of priors and by calculating lower and upper bounds of Bayes factors and posterior probabilities. Classes of intuitively meaningful priors are introduced, including unimodal densities without the constraint of symmetry for the case of precise hypotheses. Procedures for the corresponding optimizations are specified, and examples are given.