Demonstrates how to solve reliability problems using practical applications of Bayesian models
This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding.
Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more.
- Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology
- Educates managers on the potential of Bayesian reliability models and associated impact
- Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications
- Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies
- JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applications
Practical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.
YAN LIU, PHD, is Principal Reliability Engineer at Medtronic PLC, (USA). She is a certified Master Black Belt at Medtronic and has 12 years of working and consulting experience on reliability engineering and design for Six Sigma.
ATHULA I. ABEYRATNE, PHD, is Senior Principal Statistician and a certified DRM Black Belt at Medtronic PLC, (USA), where he has provided statistical consulting, training, data analyses, and modelling for 27 years.
YAN LIU博士是美敦力公司（美國）的首席可靠性工程師。她是美敦力公司的認證Master Black Belt，擁有12年的可靠性工程和Six Sigma設計的工作和諮詢經驗。
ATHULA I. ABEYRATNE博士是美敦力公司（美國）的高級主要統計師，也是認證的DRM Black Belt。他在美敦力公司提供統計諮詢、培訓、數據分析和建模工作已有27年的經驗。