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
General
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