Bayesian Inference for Multilevel Fault Tree Models

Dana Kelly () and Curtis Smith ()
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Dana Kelly: Idaho National Laboratory (INL)
Curtis Smith: Idaho National Laboratory (INL)

Chapter Chapter 12 in Bayesian Inference for Probabilistic Risk Assessment, 2011, pp 165-176 from Springer

Abstract: Abstract This chapter describes how information and data may be available at various levels in a fault tree model, and how these may be used in a Bayesian analysis 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 event (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.

Keywords: Posterior Distribution; Bayesian Inference; Failure Probability; Basic Event; Fault Tree (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-84996-187-5_12

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DOI: 10.1007/978-1-84996-187-5_12

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