Topic: Computational Bayesian Methods
Speaker: Dr. Rong Pan (Arizona State University)
Time: 9:00 AM, June 28
Venue: Room 320, Weimin Building
In the first two workshops I introduce the Bayesian methodology and its uses in solving statistical inference problems of some simple reliability models. In this workshop, I will discuss more complicated models, where analytically integrating out parameters from a joint posterior distribution, or even determining the normalizing constant of the posterior distribution, is generally not possible. Furthermore, calculating the posterior distribution of functions of parameters is difficult. Markov Chain Monte Carlo (MCMC) algorithms are a general class of computational methods used to produce samples from posterior distributions. They are often easy to implement and can be used to simulate from very high dimensional posterior distributions. We will learn the general principles of MCMC and how to implement MCMC by software to conduct Bayesian reliability data analysis in this workshop.
Biography of the Speaker:
Dr. Rong Pan received his doctoral degree in Industrial Engineering from The Pennsylvania State University, and master’s degree in Industrial Engineering from Florida A&M and Florida State Universities. He currently serves as Associate Professor of the School of Computing, Informatics, and Decision Systems Engineering, Arizona State University. He has led or participated in a number of major scientific research projects, including National Science Foundation, Design of Experiments with Dynamic Responses, Air Force Research Laboratory, Detecting Communities by Sentiment Analysis of Controversial Topics, etc.
School of Reliability and Systems Engineering