Bayesian Reliability
Michael S. Hamada
Sold by Revaluation Books, Exeter, United Kingdom
AbeBooks Seller since 6 January 2003
New - Soft cover
Condition: New
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Add to basketSold by Revaluation Books, Exeter, United Kingdom
AbeBooks Seller since 6 January 2003
Condition: New
Quantity: 2 available
Add to basket437 pages. 8.80x6.00x1.10 inches. In Stock.
Seller Inventory # x-1441926739
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:
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:
Dr. Michael S. Hamada is a Technical Staff Member in the Statistical Sciences Group at Los Alamos National Laboratory and is a Fellow of the American Statistical Association. Dr. Alyson G. Wilson is a Technical Staff Member in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. C. Shane Reese is an Associate Professor in the Department of Statistics at Brigham Young University. Dr. Harry F. Martz is retired from the Statistical Sciences Group at Los Alamos National Laboratory and is a Fellow of the American Statistical Association.
"About this title" may belong to another edition of this title.
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