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.
