Tuning of adaptive interacting particle system for rare event probability estimation

The assessment of the probability of a rare event with a naive Monte Carlo method is computationally intensive, so faster estimation or variance reduction methods are needed. We focus on one of these methods which is the interacting particle system (IPS) method. The method is not intrusive in the sense that the random Markov system under consideration is simulated with its original distribution, but selection steps are introduced that favor trajectories (particles) with high potential values. An unbiased estimator with reduced variance can then be proposed. The method requires to specify a set of potential functions. The choice of these functions is crucial because it determines the magnitude of the variance reduction. So far, little information was available on how to choose the potential functions. This paper provides the expressions of the optimal potential functions minimizing the asymptotic variance of the estimator of the IPS method and it proposes recommendations for the practical design of the potential functions.

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Tuning of adaptive interacting particle system for rare event probability estimation

Cited by 3 publications

References 37 publications

Small failure probability: principles, progress and perspectives

Small failure probability: principles, progress and perspectives

Optimizing Weighted Ensemble Sampling of Steady States

Optimal potential functions for the interacting particle system method

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