Bayesian Inference for Common Aleatory Models

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

Chapter Chapter 3 in Bayesian Inference for Probabilistic Risk Assessment, 2011, pp 15-38 from Springer

Abstract: Abstract This chapter considers aleatory models that are used frequently in probabilistic modeling situations typical of PRA. These three most commonly used aleatory models are the binomial, Poisson, and exponential distributions. For each of these three distributions, we demonstrate the Bayesian inference process for three general categories of prior distribution: conjugate, noninformative, and nonconjugate prior distributions. Lastly, we describe how prior 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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-1-84996-187-5_3

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DOI: 10.1007/978-1-84996-187-5_3

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