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Consequences of mapping data or parameters in Bayesian common-cause analysis

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  • Atwood, Corwin L.

Abstract

When mapping the common-cause alpha factor model from a group of one size to one of another size, the following facts are shown: (1) mapping data down and treating the mapped data like observed data is much too conservative; (2) mapping alpha factors down puts restrictions on the resulting alphas, so their joint distribution cannot be Dirichlet; (3) if the mapped alpha factors' posterior distributions are moderately bell-shaped, the joint distribution can be approximated well by using correlated logistic-normal conditional probabilities and (4) Bayesian mapping up is possible, but highly sensitive to the prior distribution in the top group.

Suggested Citation

  • Atwood, Corwin L., 2013. "Consequences of mapping data or parameters in Bayesian common-cause analysis," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 118-131.
    • Handle: RePEc:eee:reensy:v:118:y:2013:i:c:p:118-131
    DOI: 10.1016/j.ress.2013.04.015
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    References listed on IDEAS

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    1. Vaurio, Jussi K., 2007. "Consistent mapping of common cause failure rates and alpha factors," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 628-645.
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