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Cross-classified hierarchical Bayesian models for risk-based analysis of complex systems under sparse data

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  • Yan, Zhenyu
  • Haimes, Yacov Y.

Abstract

Decisionmaking problems in risk analysis often involve extreme events, where empirical data are usually either sparse or lacking. With sparse data, important parameters and quantities for risk and safety analyses may not be estimated and tested within an acceptable level of significance. This paper applies Hierarchical Bayesian Models (HBMs) to reduce the estimation variance and thus build relatively robust models for extreme event data through borrowing strength from different but related systems or subsystems. Based on this application, this paper further applied HBMs with cross-classified random effects (CHBMs) to address the multi-dimensional property of complex systems and borrow strength from the multiple dimensions of such systems. Case studies with both simulated and real data demonstrate the effectiveness of HBMs and CHBMs in risk-based systems analysis.

Suggested Citation

  • Yan, Zhenyu & Haimes, Yacov Y., 2010. "Cross-classified hierarchical Bayesian models for risk-based analysis of complex systems under sparse data," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 764-776.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:7:p:764-776
    DOI: 10.1016/j.ress.2010.02.014
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    References listed on IDEAS

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    5. Nima Khakzad & Sina Khakzad & Faisal Khan, 2014. "Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1759-1771, December.
    6. Hongyang Yu & Faisal Khan & Brian Veitch, 2017. "A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents," Risk Analysis, John Wiley & Sons, vol. 37(9), pages 1668-1682, September.
    7. Khakzad, Nima & Van Gelder, Pieter, 2018. "Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411.
    8. Khakzad, Nima & Khan, Faisal & Paltrinieri, Nicola, 2014. "On the application of near accident data to risk analysis of major accidents," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 116-125.
    9. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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