TY - BOOK ID - 8062632 TI - Bayesian inference for probabilistic risk assessment : a practitioner's guidebook AU - Kelly, Dana. AU - Smith, Curtis. PY - 2011 SN - 1849961867 9786613355249 1283355248 1849961875 PB - London : Springer, DB - UniCat KW - Mechanical Engineering KW - Chemical & Materials Engineering KW - Engineering & Applied Sciences KW - Technology - General KW - Materials Science KW - Industrial & Management Engineering KW - Reliability (Engineering) KW - Bayesian statistical decision theory. KW - Bayes' solution KW - Bayesian analysis KW - Reliability of equipment KW - Systems reliability KW - Engineering. KW - Statistics. KW - Quality control. KW - Reliability. KW - Industrial safety. KW - Quality Control, Reliability, Safety and Risk. KW - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. KW - Statistical decision KW - Engineering KW - Maintainability (Engineering) KW - Probabilities KW - Systems engineering KW - Plant performance KW - Safety factor in engineering KW - Structural failures KW - System safety. KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Safety, System KW - Safety of systems KW - Systems safety KW - Accidents KW - Industrial safety KW - Prevention KW - Statistics . KW - Industrial accidents KW - Industries KW - Job safety KW - Occupational hazards, Prevention of KW - Occupational health and safety KW - Occupational safety and health KW - Prevention of industrial accidents KW - Prevention of occupational hazards KW - Safety, Industrial KW - Safety engineering KW - Safety measures KW - Safety of workers KW - System safety KW - Dependability KW - Trustworthiness KW - Conduct of life KW - Factory management KW - Industrial engineering KW - Sampling (Statistics) KW - Standardization KW - Quality assurance KW - Quality of products UR - https://www.unicat.be/uniCat?func=search&query=sysid:8062632 AB - Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. ER -