Risk assessment and risk analysis are now firmly fixed in the biostatistician s and engineer's lexicon. Reliability is the other key element in the mix for smooth running projects and operations. In the modern industrial era, economic factors have resulted in the construction and operation of larger and more complex process plant. Engineers are working to maximize the benefits of modern processing technology while reducing the safety risks to acceptable levels. However, each processing plant has unique problems and each must be individually assessed to identify, evaluate and control associated hazards. Statistical methods play a key role in the quantification of reliability, and since the advent of MCMC, Bayesian methods have become increasingly important. This book addresses the need for a sound introduction to the mathematical and statistical aspects of reliability analysis from a Bayesian perspective. It features many real examples, taken from the author s vast experience, and lots of applications from reliability engineering. The author is well respected in both the statistical/Bayesian and reliability communities.