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Bayesian Inference for Common Aleatory Models

In: Bayesian Inference for Probabilistic Risk Assessment

Author

Listed:
  • Dana Kelly

    (Idaho National Laboratory (INL))

  • Curtis Smith

    (Idaho National Laboratory (INL))

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.

Suggested Citation

  • Dana Kelly & Curtis Smith, 2011. "Bayesian Inference for Common Aleatory Models," Springer Series in Reliability Engineering, in: Bayesian Inference for Probabilistic Risk Assessment, chapter 0, pages 15-38, Springer.
    • Handle: RePEc:spr:ssrchp:978-1-84996-187-5_3
    DOI: 10.1007/978-1-84996-187-5_3
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