Bayesian Reliability presents modern methods and techniques for
analyzing reliability data from a Bayesian perspective. The adoption and
application of Bayesian methods in virtually all branches of science and
engineering have significantly increased over the past few decades. This
increase is largely due to advances in simulation-based computational
tools for implementing Bayesian methods.
The authors extensively use such tools throughout this book, focusing on
assessing the reliability of components and systems with particular
attention to hierarchical models and models incorporating explanatory
variables. Such models include failure time regression models,
accelerated testing models, and degradation models. The authors pay
special attention to Bayesian goodness-of-fit testing, model validation,
reliability test design, and assurance test planning. Throughout the
book, the authors use Markov chain Monte Carlo (MCMC) algorithms for
implementing Bayesian analyses--algorithms that make the Bayesian
approach to reliability computationally feasible and conceptually
straightforward.
This book is primarily a reference collection of modern Bayesian methods
in reliability for use by reliability practitioners. There are more than
70 illustrative examples, most of which utilize real-world data. This
book can also be used as a textbook for a course in reliability and
contains more than 160 exercises.
Noteworthy highlights of the book include Bayesian approaches for the
following:
- Goodness-of-fit and model selection methods
- Hierarchical models for reliability estimation
- Fault tree analysis methodology that supports data acquisition at all
levels in the tree
- Bayesian networks in reliability analysis
- Analysis of failure count and failure time data collected from
repairable systems, and the assessment of various related performance
criteria
- Analysis of nondestructive and destructive degradation data
- Optimal design of reliability experiments
- Hierarchical reliability assurance testing
