Gopichand Agnihotram
ABSTRACT
This paper presents a comparative study of the performance of different Monte Carlo Simulation methods in the computation of rare event probabilities in Reliability Theory. We evaluate the performance of 4 well known Markov Chain Monte Carlo methods (MCMC), namely Metropolis-Hasting (MH), Hamiltonian or Hybrid Monte Carlo (HYBRID), Delayed Rejection and Adaptive Metropolis (DRAM), and Differential Evolution Adaptive Metropolis (DREAM), for computing the Probability of Failure using the Reliability Theory framework. We also compared the results of simulations with an approximate analytical method called First Order Reliability Method (FORM). The study shows that while both HYBRID and DREAM produce more accurate results, contrary to intuition, HYBRID method was very slow in performance.
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Index Terms
- A comparative study of Monte Carlo methods to compute rare event probabilities of failure in reliability models
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