標(biāo)題: Titlebook: Bayesian Inference for Probabilistic Risk Assessment; A Practitioner‘s Gui Dana Kelly,Curtis Smith Book 2011 Springer-Verlag London Limited [打印本頁] 作者: Heel-Spur 時間: 2025-3-21 18:45
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作者: 失敗主義者 時間: 2025-3-21 23:27 作者: Coronary 時間: 2025-3-22 00:55 作者: clarify 時間: 2025-3-22 06:13 作者: Pelvic-Floor 時間: 2025-3-22 11:05
Modeling Failure with Repair,s not repaired, and the component or system is replaced following failure, then the earlier analysis methods are applicable. However, in this chapter, we consider the case in which the failed component or system is repaired and placed back into service.作者: 吹牛需要藝術(shù) 時間: 2025-3-22 13:23
Bayesian Treatment of Uncertain Data,pect to observed data. For example, the number of binomial demands may not have been recorded and may have to be estimated. Similarly, the exposure time in the Poisson distribution may have to be estimated. One may not always be able to tell the exact number of failures that have occurred, because o作者: 在前面 時間: 2025-3-22 17:28 作者: 颶風(fēng) 時間: 2025-3-22 23:54
Bayesian Inference for Multilevel Fault Tree Models,ysis framework to perform probabilistic inference on the model. For example, we might have information on the overall system performance, but we might also have subsystem and component level information. We demonstrate the analysis approach using a simple fault tree model containing a single top eve作者: Obstacle 時間: 2025-3-23 01:21 作者: Axillary 時間: 2025-3-23 06:47 作者: 共同時代 時間: 2025-3-23 10:44 作者: 憂傷 時間: 2025-3-23 16:56
https://doi.org/10.1007/978-981-15-5081-2, all we know is that the failure time was longer than the duration of the test. As another example, in recording fire suppression times, the exact time of suppression may not be known; in some cases, all that may be available is an interval estimate (e.g., between 10 and 20?min). In this chapter, w作者: 使迷惑 時間: 2025-3-23 21:27
Book 2011ccompanying 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作者: Pessary 時間: 2025-3-23 22:23
1614-7839 t 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 Inference978-1-4471-2708-6978-1-84996-187-5Series ISSN 1614-7839 Series E-ISSN 2196-999X 作者: 側(cè)面左右 時間: 2025-3-24 05:19
1614-7839 ov chain Monte Carlo (MCMC) sampling.Written by experts.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 ch作者: largesse 時間: 2025-3-24 09:42 作者: monopoly 時間: 2025-3-24 14:15
More Complex Models for Random Durations,ormation criteria based on a penalized likelihood function. Also covered is the impact of parameter uncertainty on derived quantities, such as nonrecovery probabilities; failure to consider parameter uncertainty can lead to nonconservatively low estimates of such quantities, and thus to overall risk metrics that are nonconservative.作者: interference 時間: 2025-3-24 15:22 作者: 扔掉掐死你 時間: 2025-3-24 22:33 作者: HARP 時間: 2025-3-25 01:59
Book 2011hese 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 OpenB作者: Arb853 時間: 2025-3-25 06:22 作者: 短程旅游 時間: 2025-3-25 08:04
Bayesian Inference for Multilevel Fault Tree Models,nt (a “super-component”) and two sub-events (i.e., piece-parts). Also, we show how OpenBUGS can be used for the example models to estimate the probability of meeting a reliability goal at any level in the fault tree model.作者: 大火 時間: 2025-3-25 12:29
Additional Topics,prior distributions into OpenBUGS that are not included as predefined distribution choices. We close this chapter with an example of Bayesian inference for a time-dependent Markov model of pipe rupture.作者: lesion 時間: 2025-3-25 15:52
Paolo Becchi,Roberto Franzini Tibaldeoior distributions may be specified, including some cautions for developing an informative prior, and we introduce the concept of a Bayesian p-value for checking the predictions of the model against the observed data.作者: 不可磨滅 時間: 2025-3-25 22:54 作者: COLIC 時間: 2025-3-26 01:38 作者: 內(nèi)行 時間: 2025-3-26 08:06
https://doi.org/10.1007/978-1-84996-187-5Bayesian Inference; Bayesian Networks; CP6917; MCMC; Probabilistic Risk Assessment; Reliability; quality c作者: 吸引人的花招 時間: 2025-3-26 08:29
978-1-4471-2708-6Springer-Verlag London Limited 2011作者: BYRE 時間: 2025-3-26 14:49
Time Trends for Binomial and Poisson Data,t λ in the binomial and Poisson distribution, respectively. This introduces new unknown parameters and makes the Bayesian inference significantly more complicated mathematically. However, modern tools such as OpenBUGS make this analysis no less tractable than the single-parameter cases analyzed earlier.作者: mechanism 時間: 2025-3-26 18:14 作者: 松馳 時間: 2025-3-26 23:25
Modeling Failure with Repair,s not repaired, and the component or system is replaced following failure, then the earlier analysis methods are applicable. However, in this chapter, we consider the case in which the failed component or system is repaired and placed back into service.作者: indecipherable 時間: 2025-3-27 03:29
Dana Kelly,Curtis SmithFormulates complex problems without becoming weighed down by mathematical detail.Presents a modern perspective of Bayesian networks and Markov chain Monte Carlo (MCMC) sampling.Written by experts作者: AFFIX 時間: 2025-3-27 06:26 作者: delusion 時間: 2025-3-27 13:16 作者: locus-ceruleus 時間: 2025-3-27 13:41
Human Dignity, Ubuntu and Global Justice,This chapter describes the interpretation of the components of Bayes’ Theorem. The relevant parts of the theorem are described, and a simple example is demonstrated using both a discrete and continuous prior distribution.作者: 來這真柔軟 時間: 2025-3-27 18:40
https://doi.org/10.1007/978-981-15-5081-2This chapter discusses the Bayesian framework for expanding common likelihood functions introduced in earlier chapters to include additional variability. This variability can be over time, among sources, etc.作者: Onerous 時間: 2025-3-27 23:09 作者: Integrate 時間: 2025-3-28 02:39
Introduction to Bayesian Inference,This chapter describes the interpretation of the components of Bayes’ Theorem. The relevant parts of the theorem are described, and a simple example is demonstrated using both a discrete and continuous prior distribution.作者: 違抗 時間: 2025-3-28 06:33 作者: ERUPT 時間: 2025-3-28 13:11 作者: 殺子女者 時間: 2025-3-28 16:34
Joaquim Bosch-Barrera,Juan Vidal Botat λ in the binomial and Poisson distribution, respectively. This introduces new unknown parameters and makes the Bayesian inference significantly more complicated mathematically. However, modern tools such as OpenBUGS make this analysis no less tractable than the single-parameter cases analyzed earlier.作者: 惹人反感 時間: 2025-3-28 19:17 作者: synchronous 時間: 2025-3-29 03:00
https://doi.org/10.1007/978-981-15-5081-2s not repaired, and the component or system is replaced following failure, then the earlier analysis methods are applicable. However, in this chapter, we consider the case in which the failed component or system is repaired and placed back into service.作者: 弓箭 時間: 2025-3-29 03:40 作者: infatuation 時間: 2025-3-29 07:33
Joaquim Bosch-Barrera,Juan Vidal Botat λ in the binomial and Poisson distribution, respectively. This introduces new unknown parameters and makes the Bayesian inference significantly more complicated mathematically. However, modern tools such as OpenBUGS make this analysis no less tractable than the single-parameter cases analyzed earl作者: resuscitation 時間: 2025-3-29 14:02
Taking Human Dignity More Humanelystimation, the analyst must have reasonable assurance that the Markov chain(s) used to generate the samples has converged to the posterior distribution. This chapter presents qualitative and quantitative convergence checks that an analyst can use to obtain this assurance and avoid pitfalls caused by作者: ARC 時間: 2025-3-29 16:04 作者: 征兵 時間: 2025-3-29 20:43
https://doi.org/10.1007/978-981-15-5081-2s not repaired, and the component or system is replaced following failure, then the earlier analysis methods are applicable. However, in this chapter, we consider the case in which the failed component or system is repaired and placed back into service.作者: NEG 時間: 2025-3-30 03:51
https://doi.org/10.1007/978-981-15-5081-2pect to observed data. For example, the number of binomial demands may not have been recorded and may have to be estimated. Similarly, the exposure time in the Poisson distribution may have to be estimated. One may not always be able to tell the exact number of failures that have occurred, because o
