Bayesian Reliability by Michael S. Hamada, Alyson Wilson, C. Shane Reese, Harry

By Michael S. Hamada, Alyson Wilson, C. Shane Reese, Harry Martz

Bayesian Reliability provides sleek tools and methods for studying reliability facts from a Bayesian point of view. The adoption and alertness of Bayesian equipment in almost all branches of technology and engineering have considerably elevated over the last few a long time. This bring up is basically as a result of advances in simulation-based computational instruments for enforcing Bayesian equipment.

The authors greatly use such instruments all through this booklet, concentrating on assessing the reliability of parts and structures with specific awareness to hierarchical types and types incorporating explanatory variables. Such versions comprise failure time regression types, speeded up checking out versions, and degradation versions. The authors pay particular awareness to Bayesian goodness-of-fit trying out, version validation, reliability try out layout, and coverage try making plans. in the course of the e-book, the authors use Markov chain Monte Carlo (MCMC) algorithms for enforcing Bayesian analyses--algorithms that make the Bayesian method of reliability computationally possible and conceptually straightforward.

This e-book is essentially a reference selection of glossy Bayesian equipment in reliability to be used through reliability practitioners. There are greater than 70 illustrative examples, such a lot of which make the most of real-world info. This booklet is additionally used as a textbook for a direction in reliability and includes greater than a hundred and sixty exercises.

Noteworthy highlights of the publication contain Bayesian techniques for the following:

  • Goodness-of-fit and version choice methods
  • Hierarchical versions for reliability estimation
  • Fault tree research method that helps facts acquisition in any respect degrees within the tree
  • Bayesian networks in reliability analysis
  • Analysis of failure count number and failure time facts accumulated from repairable structures, and the review of varied comparable functionality criteria
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  • Analysis of nondestructive and damaging degradation data
  • Optimal layout of reliability experiments
  • Hierarchical reliability coverage testing

Dr. Michael S. Hamada is a Technical employees Member within the Statistical Sciences team at Los Alamos nationwide Laboratory and is a Fellow of the yank Statistical organization. Dr. Alyson G. Wilson is a Technical employees Member within the Statistical Sciences crew at Los Alamos nationwide Laboratory. Dr. C. Shane Reese is an affiliate Professor within the division of statistics at Brigham younger collage. Dr. Harry F. Martz is retired from the Statistical Sciences staff at Los Alamos nationwide Laboratory and is a Fellow of the yankee Statistical Association.

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Sample text

Not surprisingly, we call the logarithm of the likelihood function the log-likelihood function. When observations are conditionally independent, the log-likelihood function is mathematically easier to handle than the likelihood function because it takes the form of a sum rather than of a product. The log-likelihood function is the sum of the logarithm of the density values evaluated at each observation, whereas the likelihood function is the product of the sampling density evaluated at each observation.

This is known as independent censoring or noninformative censoring. For example, if an air conditioner is removed from our study because it is sold to a customer, then the censoring of its failure time is independent of its survival time. Because Bayesian modeling uses the observed lifetime in its analyses, censoring mechanisms are easily addressed. Suppose that we observe that an item has failed before time tL , and its lifetime data are left censored. We know that its lifetime is in [0, tL ].

6. 5 Bayesian Reliability Analysis The acceptance and applicability of Bayesian methods have increased in recent years. Today, with advances in computation and methodology, researchers are using Bayesian methods to solve an increasing variety of complex problems. In many applications, Bayesian methods provide important computational and methodological advantages over classical techniques. This book focuses on Bayesian reliability analysis, which includes the topics of modeling, computation, sensitivity analysis, and model checking.

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