Practical Applications of Bayesian Reliability

Preface

Recently, groundbreaking work using Bayesian statistics for reliability analysis has emerged at various seminars and technical conferences which demonstrated great power in accurately predicting reliability and/or reducing sample size. Many engineers and scientists have expressed interest in learning Bayesian statistics. However, there is also much confusion in the learning process. This confusion comes mainly from three aspects:

1. What is Bayesian analysis exactly?
2. What are the benefits?
3. How can I apply the methods to solve my own problems?

This book is intended to provide basic knowledge and practical examples of Bayesian modeling in reliability and related science and engineering practices. We hope it will help engineers and scientists to find answers to the above common questions.

For scientists and engineers with no programming experience, coding is often considered too daunting. To help readers get started quickly, many Bayesian models using Just Another Gibbs Sampler (JAGS) are provided in this book (e.g. 3.4_Weibull.JAGS in Section 3.4) containing fewer than ten lines of commands. Then all you need to do is to learn a few functions to run the Bayesian model and diagnose the results (discussed in Section 3.4). To help readers become familiar with R coding, this book also provides a number of short R scripts consisting of simple functions. There are some cases requiring longer R scripts. Those programs are divided into a few sections, and the function of each section ...