LEADER 05352nam 22007815 450 001 9910969465003321 005 20250504232912.0 010 $a1-281-86123-5 010 $a9786611861230 010 $a0-387-77950-7 024 7 $a10.1007/978-0-387-77950-8 035 $a(CKB)1000000000491957 035 $a(EBL)364398 035 $a(OCoLC)288468936 035 $a(SSID)ssj0000109313 035 $a(PQKBManifestationID)11138438 035 $a(PQKBTitleCode)TC0000109313 035 $a(PQKBWorkID)10045437 035 $a(PQKB)11080136 035 $a(DE-He213)978-0-387-77950-8 035 $a(MiAaPQ)EBC364398 035 $a(Au-PeEL)EBL364398 035 $a(CaPaEBR)ebr10245957 035 $a(CaONFJC)MIL186123 035 $a(PPN)128122730 035 $a(EXLCZ)991000000000491957 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian Reliability /$fby Michael S. Hamada, Alyson Wilson, C. Shane Reese, Harry Martz 205 $a1st ed. 2008. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2008. 215 $a1 online resource (449 p.) 225 1 $aSpringer Series in Statistics,$x2197-568X 300 $aDescription based upon print version of record. 311 08$a0-387-77948-5 320 $aIncludes bibliographical references and indexes. 327 $aReliability Concepts -- Bayesian Inference -- Advanced Bayesian Modeling and Computational Methods -- Component Reliability -- System Reliability -- Repairable System Reliability -- Regression Models in Reliability -- Using Degradation Data to Assess Reliability -- Planning for Reliability Data Collection -- Assurance Testing. 330 $aBayesian 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 methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria < Analysis of nondestructive and destructive degradation data Optimal design of reliability experiments Hierarchical reliability assurance testing Dr. Michael S. Hamada is a Technical Staff Member in the Statistical Sciences Group at Los Alamos National Laboratory and is a Fellow of the American Statistical Association. Dr. Alyson G. Wilson is a Technical Staff Member in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. C. Shane Reese is an Associate Professor in the Department of Statistics at Brigham Young University. Dr. Harry F. Martz is retired from the Statistical Sciences Group at Los Alamos National Laboratory and is a Fellow of the American Statistical Association. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aProbabilities 606 $aStatistics 606 $aSecurity systems 606 $aStatistics 606 $aProbability Theory 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aSecurity Science and Technology 606 $aStatistical Theory and Methods 615 0$aProbabilities. 615 0$aStatistics. 615 0$aSecurity systems. 615 0$aStatistics. 615 14$aProbability Theory. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aSecurity Science and Technology. 615 24$aStatistical Theory and Methods. 676 $a620.0045201519542 701 $aHamada$b Michael$f1955-$01821343 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910969465003321 996 $aBayesian Reliability$94385194 997 $aUNINA