Computing Bounds on Expectations
本文提出一种蒙特卡洛方法，用于计算贝叶斯分析中先验和后验量的界限，解决高维参数空间下稳健贝叶斯推断的计算难题，对统计学家和机器学习研究者有用。
Abstract One method for evaluating the sensitivity of a Bayesian analysis is to embed the prior into a class of priors. Then bounds on prior and posterior quantities of interest must be computed. This approach to inference, often called robust Bayesian inference, has received much attention lately. Implementing robust Bayesian methods entails difficult computations, especially if the parameter space is high dimensional. In this article we develop a Monte Carlo approach to computing these bounds and also explore some interesting theoretical properties of certain classes of priors. The methods can be useful in other situations in which bounds on expectations are required.