Bayesian Reliability (Springer Series in Statistics)

Bayesian Reliability presents a comprehensive overview of modern methods and techniques for analyzing reliability data from a Bayesian perspective. As the adoption of Bayesian methods has grown across various branches of science and engineering, this book offers valuable insights into their application, supported by recent advances in simulation-based computational tools. The authors emphasize the practical implementation of these methods, making complex concepts accessible to reliability practitioners.

Throughout the book, the authors extensively utilize simulation tools, particularly Markov chain Monte Carlo (MCMC) algorithms, to facilitate Bayesian analyses. These algorithms make the Bayesian approach to reliability both computationally feasible and conceptually straightforward. The focus is 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.

A key feature of this publication is its emphasis on Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. The authors provide over 70 illustrative examples, most of which utilize real-world data, to demonstrate the practical application of the methods discussed. Additionally, the book serves as a valuable resource for educators, containing more than 160 exercises suitable for courses in reliability.

Noteworthy highlights include Bayesian approaches for goodness-of-fit and model selection methods, hierarchical models for reliability estimation, and fault tree analysis methodology that supports data acquisition at all levels in the tree. The book also explores the use of Bayesian networks in reliability analysis, as well as the analysis of failure count and failure time data collected from repairable systems, including the assessment of various related performance criteria. Furthermore, it covers the analysis of nondestructive and destructive degradation data, the optimal design of reliability experiments, and hierarchical reliability assurance testing.

• Title: ABIS_BOOK
• Author: Michael S. Hamada
• Genre: Mathematics
• Series: Springer Series in Statistics
• Format: Hardcover
• Language: English
• Publication Year: 2008
• Pages: 452