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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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    Citations

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    3. Jin‐Zhu Yu & Hiba Baroud, 2019. "Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1930-1948, September.
    4. J. S. Busby & B. Green & D. Hutchison, 2017. "Analysis of Affordance, Time, and Adaptation in the Assessment of Industrial Control System Cybersecurity Risk," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1298-1314, July.
    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).
    10. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    11. Yacov Y. Haimes, 2012. "Modeling complex systems of systems with Phantom System Models," Systems Engineering, John Wiley & Sons, vol. 15(3), pages 333-346, September.
    12. Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. Shengli, Liu & Yongtu, Liang, 2019. "Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).

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