Bayes Factors for Nonlinear Hypotheses and Likelihood Distributions
提出了计算参数非线性约束假设下贝叶斯因子的新方法，利用投影技术诱导约束参数空间的先验分布，并引入多种距离度量（包括Kullback-Leibler散度），适用于逻辑回归等模型。
New methods are proposed which allow Bayes factors to be computed for hypotheses which impose nonlinear restrictions on the parameters. Projection methods are used to induce the prior distribution over the restricted parameter space which is required for computation of the Bayes factor. Various distance metrics are introduced to define the projection, including a utility-based metric which gives Kullback-Leibler divergence as a special case. Draws from the restricted and unrestricted prior distributions are used to construct marginal distributions of the likelihood which is shown to have additional diagnostic value over and above the Bayes factor. These methods are applied to hypotheses in logistic regression.