Bayesian Monte Carlo method

M. Rajabalinejad*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1050-1060
Number of pages11
JournalReliability Engineering and System Safety
Issue number10
Publication statusPublished - Oct 2010
Externally publishedYes


  • Bayesian Monte Carlo
  • Dynamic bounds
  • Reliability analysis
  • Simulation


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