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.
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