Bayesian Regression Models

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

Chapter Chapter 11 in Bayesian Inference for Probabilistic Risk Assessment, 2011, pp 141-163 from Springer

Abstract: Abstract Sometimes a parameter in an aleatory model, such as p in the binomial distribution or λ in the Poisson distribution, can be affected by observable quantities such as pressure, mass, or temperature. For example, in the case of a pressure vessel, very high pressure and high temperature may be leading indicators of failures. In such cases, information about the explanatory variables can be used in the Bayesian inference paradigm to inform the estimates of p or λ. We have already seen examples of this in Chap. 5, where we modeled the influence of time on p and λ via logistic and loglinear regression models, respectively. In this chapter, we extend this concept to more complex situations, such as a Bayesian regression approach that estimates the probability of O-ring failure in the solid-rocket booster motors of the space shuttle.

Keywords: Failure Time; Credible Interval; Deviance Information Criterion; Accelerate Life Testing; Lognormal Model (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_11

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DOI: 10.1007/978-1-84996-187-5_11

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