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Bayesian Monte Carlo method

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  • Rajabalinejad, M.

Abstract

To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by using BMC to further reduce cost of simulations. This study suggests speeding up the simulation process by considering the logical dependence of neighboring points as prior information. This information is used in the BMC method to produce a predictive tool through the simulation process. The general methodology and algorithm of BMC method are presented in this paper. The BMC method is applied to the simplified break water model as well as the finite element model of 17th Street Canal in New Orleans, and the results are compared with the MC and Dynamic Bounds methods.

Suggested Citation

  • Rajabalinejad, M., 2010. "Bayesian Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1050-1060.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:10:p:1050-1060
    DOI: 10.1016/j.ress.2010.04.014
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    References listed on IDEAS

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    1. Mohammadreza Rajabalinejad & Zeki Demirbilek & Tewfik Mahdi, 2010. "Determination of failure probabilities of flood defence systems with improved dynamic bounds method," 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. 55(1), pages 95-109, October.
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    Cited by:

    1. Mohammadreza Rajabalinejad & Zeki Demirbilek & Tewfik Mahdi, 2010. "Determination of failure probabilities of flood defence systems with improved dynamic bounds method," 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. 55(1), pages 95-109, October.
    2. Mohammadreza Rajabalinejad & Tew-Fik Mahdi, 2010. "The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations," 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. 55(1), pages 29-49, October.
    3. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Mohammadreza Rajabalinejad & Tewfik Mahdi & Pieter Gelder, 2010. "Stochastic methods for safety assessment of the flood defense system in the Scheldt Estuary of the Netherlands," 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. 55(1), pages 123-144, October.

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