Bayesian reliability analysis
A comprehensive collection of and introduction to the major advances in Bayesian reliability analysis techniques developed during the last two decades, in textbook form. Focuses primary attention on the exponential, Weibull, normal, log normal, inverse Gaussian, and gamma failure time distributions, as well as the binomial, Pascal, and Poisson sampling models. Noninformative and natural conhugate prior distributions are emphasized, although other classes or prior distributions are also often considered. Background chapters on probability, statistics, and classical reliability analysis methods are also included.
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Elements of Statistics
Elements of Reliability
17 other sections not shown
assumed Bayes estimator Bayes point estimate Bayesian estimation Bayesian Inference Bayesian point estimator Bayesian reliability binomial Canavos Chapter computed conjugate prior consider corresponding decision function defined denote determine EB estimators EB risk Empirical Bayes equations estimate of system Example exponential distribution gamma distribution gamma prior graph hazard rate improper prior independent integral interval estimates LBPI likelihood function marginal distribution Martz mean and variance Mellin transform methods minimum Bayes risk ML estimator MTTF noninformative prior distribution number of failures observed obtained operating parallel system percentile plot posterior distribution posterior mean posterior pdf posterior risk posterior variance prior and posterior prior distribution prior mean prior pdf procedure random reliability function respectively sampling distribution Section selected shape parameter specified squared-error loss function subsystem sufficient statistic Suppose symmetric system availability system reliability Table TBPI estimate test data theorem tion Tsokos values Weibull Weibull distribution ZO'T