Mathematical Sciences: Bayesian Inference and Computing

将开发评估结果敏感性的工具 关于先验概率分布的假设（ 表示从数据以外的来源获得的知识 分析）。更深入地了解 统计模型的参数化将通过 发展诊断方法， 正态分布与另一个分布的近似。 重点将是后验分布的近似 这是贝叶斯分析中推断的基础。 将纳入适当的数值积分策略 在计算环境中进行贝叶斯分析。进一步工作 将进行渐近贝叶斯推理， 非参数问题，其中的假设 统计模型被大大削弱。

Tools will be developed for assessing sensitivity of results to assumptions about the prior probability distribution (which represents knowledge gained from sources other than the data being analyzed). A deeper understanding of the role of parameterization of statistical models will be gained through the development of diagnostics for the detection of a poor approximation by a Normal distribution to another distribution. The focus will be the approximation of the posterior distribution which is the basis for inference in a Bayesian analysis. Appropriate numerical integration strategies will be incorporated in a computing environment for Bayesian analysis. Further work will be carried out on asymptotic Bayesian inference for nonparametric problems in which the assumptions of the statistical model are greatly weakened.