Moving Particles: a parallel optimal Multilevel Splitting method with application in quantiles estimation and meta-model based algorithms

TL;DR

This paper introduces a parallel optimal Multilevel Splitting method based on particle moves, significantly reducing sample requirements for rare event probability estimation and improving quantile and meta-model algorithms.

Contribution

It proposes a novel particle move approach to Multilevel Splitting, eliminating the need for nested subsets and enhancing efficiency in rare event probability estimation.

Findings

Sample requirement follows a Poisson law with parameter log(1/p)

The method enables a parallel optimal Multilevel Splitting algorithm without predefined subsets

New strategies for quantile estimation and design of experiments in meta-models are developed.

Abstract

Considering the issue of estimating small probabilities p, ie. measuring a rare domain F = {x | g(x) > q} with respect to the distribution of a random vector X, Multilevel Splitting strategies (also called Subset Simulation) aim at writing F as an intersection of less rare events (nested subsets) such that their measures are conditionally easily computable. However the definition of an appropriate sequence of nested subsets remains an open issue. We introduce here a new approach to Multilevel Splitting methods in terms of a move of particles in the input space. This allows us to derive two main results: (1) the number of samples required to get a realisation of X in F is drastically reduced, following a Poisson law with parameter log 1/p (to be compared with 1/p for naive Monte-Carlo); and (2) we get a parallel optimal Multilevel Splitting algorithm where there is indeed no subset to define any more. We also apply result (1) in quantile estimation producing a new parallel algorithm and derive a new strategy for the construction of first Design Of Experiments in meta-model based algorithms.