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Practical applications of bayesian reliability

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This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding.

Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more

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Detail Information

Series Title

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Call Number

620.004 LIU p

Publisher

: John Wiley & Sons., 2019

Collation

320 hal : ilus : 24 cm

Language

English

ISBN/ISSN

9781119287971

Classification

620.004

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

reliability engineering

Specific Detail Info

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Statement of Responsibility

Yan Liu, Athula I. Abeyratne

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