General Bayesian Methods for Typical Reliability Data Analysis

Ming Li

Ming Li is a statistician at the Applied Statistics Lab, GE Global Research Center. He obtained his Statistics PhD in 2010 from Iowa State University and he also held advanced degrees in Physics. His current research interests include reliability, quality control, physics model assisted statistical analysis, and applied statistics to industry, engineering, and business applications. He has more


Statistical methods are usually used in reliability analysis due to the uncertainty and distribution nature of reliability data. Bayesian analysis has been part of statistical analysis from the very beginning when the foundations of modern statistics were established. Bayesian methods, however, are rarely used in the analysis of reliability data, mainly due to the lack of user friendly and efficient computation tools. With the development of freely available and efficient software package WinBUGs and OpenBUGs, there are more and more statisticians and engineers using Bayesian's idea to combine useful prior information and the field data. In this talk, we first briefly review several main areas in statistical reliability analysis, then introduce the basic ideas of the Bayesian method and WinBUGs / OpenBUGs software. Next we will show how to apply Bayesian methods to several typical reliability problems through WinBUGs / OpenBUGs. Finally some common mistakes and pitfalls for Bayesian application to statistical reliability data analysis are discussed.This is a joint work with Professor William Q. Meeker at Department of Statistics of Iowa State University

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