Posterior Cumulant Relationships in Bayesian Inference Involving the Exponential Family
针对似然或先验为指数族形式的单参数贝叶斯推断，推导了后验矩和累积量的关系，推广了共轭分析中的简单关系，并应用于贝叶斯稳健性和近似。
Abstract For Bayesian inference in one-parameter contexts where either the likelihood or the prior has an exponential family form, relationships are derived for posterior moments and cumulants of (functions of) both the canonical and the expectation parameters. The identities exhibited generalize the simple relationships well known in the conjugate analysis case. Applications of these results are indicated in the areas of Bayesian robustness and approximation. In particular, results are obtained on the behavior of the posterior distribution for a large observation, generalizing work of Meeden and Isaacson.