Parallel Computing in Bayesian Inference

本研究旨在整合贝叶斯统计 推理和决策理论与现代计算和 数值方法 研究计划的核心是 并行/分布式处理方法的调整 贝叶斯统计与决策问题的计算 理论 本研究将有三个主要方向：1） 贝叶斯方法论的理论发展，2）研究 并行/分布式计算算法，以及3） 数值技术在贝叶斯问题中的应用。 的 方法，算法和软件开发， 并行/分布式处理将是重要的， 各种理论和实际贝叶斯问题。 领域 应用程序将包括分层贝叶斯建模， 灵敏度和稳健性，序贯分析， 贝叶斯方法在顺序过程控制中的应用 图像重建和贝叶斯推理神经网络。

This research is designed to integrate Bayesian statistical inference and decision theory with modern computational and numerical methods. The core of the research program is the adaptation of methods for parallel/distributed processing of computation to problems in Bayesian statistics and decision theory. The research will have three principle directions: 1) theoretical development of the Bayesian methodology, 2) research into parallel/distributed computing algorithms, and 3) applications of numerical techniques to Bayesian problems. The methods, algorithms, and software to be developed for parallel/distributed processing will be important in a wide variety of theoretical and practical Bayesian problems. Areas of application will include hierarchical Bayesian modeling, sensitivity and robustness, sequential analysis with application in sequential process control, Bayesian methods for image reconstruction, and Bayesian inference for neural networks.