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 smooth equipment and methods for studying reliability info from a Bayesian point of view. The adoption and alertness of Bayesian equipment in nearly all branches of technological know-how and engineering have considerably elevated during the last few many years. This bring up is basically as a result of advances in simulation-based computational instruments for enforcing Bayesian equipment.

The authors generally use such instruments all through this ebook, targeting assessing the reliability of parts and structures with specific consciousness to hierarchical versions and types incorporating explanatory variables. Such types contain failure time regression types, speeded up checking out types, and degradation types. The authors pay targeted realization to Bayesian goodness-of-fit checking out, version validation, reliability attempt layout, and coverage try out making plans. through the publication, the authors use Markov chain Monte Carlo (MCMC) algorithms for imposing Bayesian analyses--algorithms that make the Bayesian method of reliability computationally possible and conceptually straightforward.

This booklet is basically a reference number of smooth Bayesian equipment in reliability to be used via reliability practitioners. There are greater than 70 illustrative examples, such a lot of which make the most of real-world facts. This publication can be used as a textbook for a direction in reliability and comprises greater than one hundred sixty exercises.

Noteworthy highlights of the ebook comprise Bayesian ways for the following:

  • Goodness-of-fit and version choice methods
  • Hierarchical types 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 information accrued from repairable platforms, and the review of varied similar functionality criteria
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  • Analysis of nondestructive and harmful degradation data
  • Optimal layout of reliability experiments
  • Hierarchical reliability insurance 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 yankee Statistical organization. Dr. Alyson G. Wilson is a Technical employees Member within the Statistical Sciences team at Los Alamos nationwide Laboratory. Dr. C. Shane Reese is an affiliate Professor within the division of information at Brigham younger college. Dr. Harry F. Martz is retired from the Statistical Sciences workforce at Los Alamos nationwide Laboratory and is a Fellow of the yankee Statistical Association.

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Extra info for Bayesian Reliability

Example text

This statement differs at a fundamental, philosophical level from statements made in classical hypothesis testing. Under the classical paradigm, probabilities regarding the truth of a model are not cited. Instead, probability statements made from within the classical paradigm refer only to the probability of observing a test statistic more extreme than the one actually observed. Such statements do not directly address the question of whether a particular model is true. There is an important proviso regarding the use of Bayes’ factors for model testing: Bayes’ factors are only defined when proper prior distributions are used.

In this simple setting, the exact confidence interval can also be calculated by finding values of π for which more than two successes or fewer than four successes would be observed in 5% of samples, respectively. 564). Consistency and efficiency are properties of estimators most relevant for estimation when sample sizes are large. However, because sample sizes available for estimation are never infinite, inference for small-to-moderate sample sizes is also important. In this regard, the large sample properties of the MLE do not pertain in more complicated settings.

Such relevant information is an extremely useful and powerful component in the Bayesian approach, and thoughtful Bayesian parameter estimates reflect this knowledge. This relevant information is often derived from combinations of such sources as physical/chemical theory, engineering and qualification test results, generic industrywide reliability data, computational analysis, past experience with similar devices, previous test results obtained from a process development program, and the subjective judgment of experienced personnel.

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