Bayesian Reliability by Hamada Michael (28 results)

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Taschenbuch. Condition: Neu. Bayesian Reliability | Michael S. Hamada (u. a.) | Taschenbuch | xvi | Englisch | 2010 | Springer | EAN 9781441926739 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condition: New. pp. 456.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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:Goodness-of-fit and model selection methodsHierarchical models for reliability estimationFault tree analysis methodology that supports data acquisition at all levels in the treeBayesian networks in reliability analysisAnalysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteriaAnalysis of nondestructive and destructive degradation dataOptimal design of reliability experimentsHierarchical reliability assurance testing.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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:Goodness-of-fit and model selection methodsHierarchical models for reliability estimationFault tree analysis methodology that supports data acquisition at all levels in the treeBayesian networks in reliability analysisAnalysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteriaAnalysis of nondestructive and destructive degradation dataOptimal design of reliability experimentsHierarchical reliability assurance testing.

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paperback. Condition: New. Paperback. Pub Date :2014-04-01 Pages: 324 Language: Chinese Publisher: National Defense Industry Press. Reliability. Maintainability and Supportability academic monographs Renditions: Bayesian reliability also focuses on the Bayesian theory in reliability Other applications of the field. specifically including the goodness of fit test. model validation. reliability test program design. Because of the Bayesian model analytical solution is generally very difficult. Reliability. Maintainability.

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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:Goodness-of-fit and model selection methodsHierarchical models for reliability estimationFault tree analysis methodology that supports data acquisition at all levels in the treeBayesian networks in reliability analysisAnalysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteriaAnalysis of nondestructive and destructive degradation dataOptimal design of reliability experimentsHierarchical reliability assurance testing 436 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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:Goodness-of-fit and model selection methodsHierarchical models for reliability estimationFault tree analysis methodology that supports data acquisition at all levels in the treeBayesian networks in reliability analysisAnalysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteriaAnalysis of nondestructive and destructive degradation dataOptimal design of reliability experimentsHierarchical reliability assurance testing 456 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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:Goodness-of-fit and model selection methodsHierarchical models for reliability estimationFault tree analysis methodology that supports data acquisition at all levels in the treeBayesian networks in reliability analysisAnalysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteriaAnalysis of nondestructive and destructive degradation dataOptimal design of reliability experimentsHierarchical reliability assurance testingSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 456 pp. Englisch.

Condition: New. Print on Demand pp. 456 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 456.