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Aggregation Error in Bayesian Analysis of Reliability Systems

Author

Listed:
  • M. Naceur Azaiez

    (Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madison, Wisconsin 53706)

  • Vicki M. Bier

    (Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madison, Wisconsin 53706)

Abstract

Perfect aggregation in Bayesian system reliability analysis has been shown to be extremely unlikely. In other words, aggregation error is almost inevitable. Consequently, analysts have to deal with the following dilemma: on one hand, an aggregate analysis (i.e., an analysis at the system level), while relatively inexpensive, may be misleading. On the other hand, a disaggregate analysis (i.e., at the component level) provides more accurate results, but may be costly and impractical. Therefore, simple techniques to estimate the size of aggregation error are necessary to help analysts choose the most appropriate level of detail for an analysis. In this paper, reasonable bounds on the aggregation error are derived for a variety of reliability models. In particular, these bounds will never be more than twice the actual error. Tools to compute these bounds (and in some cases the actual error) are also provided.

Suggested Citation

  • M. Naceur Azaiez & Vicki M. Bier, 1996. "Aggregation Error in Bayesian Analysis of Reliability Systems," Management Science, INFORMS, vol. 42(4), pages 516-528, April.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:4:p:516-528
    DOI: 10.1287/mnsc.42.4.516
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    Cited by:

    1. Wu, Shaomin, 2019. "A failure process model with the exponential smoothing of intensity functions," European Journal of Operational Research, Elsevier, vol. 275(2), pages 502-513.
    2. Asma Ben Yaghlane & Mohamed Naceur Azaiez, 2019. "System survivability to continuous attacks: A game theoretic setting for constant attack rate processes," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1308-1320, August.

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