Consensus-based rare event estimation

TL;DR

This paper presents a novel consensus-based adaptive importance sampling algorithm for rare event estimation, utilizing particle dynamics governed by McKean-Vlasov stochastic differential equations, with automatic parameter updates and competitive numerical performance.

Contribution

The paper introduces a new consensus-based importance sampling method with automatic parameter tuning, including a novel time step controller, for efficient rare event estimation.

Findings

Method is competitive with state-of-the-art algorithms

Automatic parameter updates improve efficiency

Numerical experiments validate effectiveness

Abstract

In this paper, we introduce a new algorithm for rare event estimation based on adaptive importance sampling. We consider a smoothed version of the optimal importance sampling density, which is approximated by an ensemble of interacting particles. The particle dynamics is governed by a McKean-Vlasov stochastic differential equation, which was introduced and analyzed in (Carrillo et al., Stud. Appl. Math. 148:1069-1140, 2022) for consensus-based sampling and optimization of posterior distributions arising in the context of Bayesian inverse problems. We develop automatic updates for the internal parameters of our algorithm. This includes a novel time step size controller for the exponential Euler method, which discretizes the particle dynamics. The behavior of all parameter updates depends on easy to interpret accuracy criteria specified by the user. We show in numerical experiments that our method is competitive to state-of-the-art adaptive importance sampling algorithms for rare event estimation, namely a sequential importance sampling method and the ensemble Kalman filter for rare event estimation.