000			03710nam a22004815i 4500
001			978-1-84996-187-5
003			DE-He213
005			20140220083736.0
007			cr nn 008mamaa
008			110829s2011 xxk| s |||| 0|eng d
020			_a9781849961875 _9978-1-84996-187-5
024	7		_a10.1007/978-1-84996-187-5 _2doi
050		4	_aTA169.7
050		4	_aT55-T55.3
050		4	_aTA403.6
072		7	_aTGPR _2bicssc
072		7	_aTEC032000 _2bisacsh
082	0	4	_a658.56 _223
100	1		_aKelly, Dana. _eauthor.
245	1	0	_aBayesian Inference for Probabilistic Risk Assessment _h[electronic resource] : _bA Practitioner's Guidebook / _cby Dana Kelly, Curtis Smith.
264		1	_aLondon : _bSpringer London, _c2011.
300			_aXII, 228 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Reliability Engineering, _x1614-7839
505	0		_a1. Introduction and Motivation -- 2. Introduction to Bayesian Inference -- 3. Bayesian Inference for Common Aleatory Models -- 4. Bayesian Model Checking -- 5. Time Trends for Binomial and Poisson Data -- 6. Checking Convergence to Posterior Distribution -- 7. Hierarchical Bayes Models for Variability -- 8. More Complex Models for Random Durations -- 9. Modeling Failure with Repair -- 10. Bayesian Treatment of Uncertain Data -- 11. Bayesian Regression Models -- 12. Bayesian Inference for Multilevel Fault Tree Models -- 13. Additional Topics.
520			_aBayesian 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.
650		0	_aEngineering.
650		0	_aSystem safety.
650	1	4	_aEngineering.
650	2	4	_aQuality Control, Reliability, Safety and Risk.
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700	1		_aSmith, Curtis. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781849961868
830		0	_aSpringer Series in Reliability Engineering, _x1614-7839
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84996-187-5
912			_aZDB-2-ENG
999			_c106455 _d106455