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Bayesian Regression Models

In: Bayesian Inference for Probabilistic Risk Assessment

Author

Listed:
  • Dana Kelly

    (Idaho National Laboratory (INL))

  • Curtis Smith

    (Idaho National Laboratory (INL))

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.

Suggested Citation

  • Dana Kelly & Curtis Smith, 2011. "Bayesian Regression Models," Springer Series in Reliability Engineering, in: Bayesian Inference for Probabilistic Risk Assessment, chapter 0, pages 141-163, Springer.
    • Handle: RePEc:spr:ssrchp:978-1-84996-187-5_11
    DOI: 10.1007/978-1-84996-187-5_11
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    Cited by:

    1. Nascimento, Marcus Gerardus Lavagnole & Abanto-Valle, Carlos Antonio & Mendonça, Mario Jorge, 2019. "Multivariate Spatial IV Regression," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(2), January.
    2. Nascimento, Marcus Gerardus Lavagnole & Abanto-Valle, Carlos Antonio & Mendonça, Mario Jorge, 2018. "Multivariate Spatial IV Regression," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(2).

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