Rare-Event Detection by Quasi-Wang-Landau Monte Carlo Sampling with Approximate Bayesian Computation

Abstract

We propose a new rare-event detection method based on quasi-Wang-Landau Monte Carlo (QWLMC) sampling with approximate Bayesian computation (ABC) called QWLMC-ABC. QWLMC-ABC integrates ABC and a Halton sequence into Wang-Landau Monte Carlo (WLMC) sampling methods. The Halton sequence provides an improved proposal function and increases the accuracy of WLMC sampling, which results in QWLMC sampling. ABC approximates a likelihood function and boosts the speed of QWLMC sampling, which yields QWLMC-ABC. QWLMC-ABC is applied to estimate the rareness of events in a statistical manner. Experimental results demonstrate that our method is comparable to state-of-the-art methods. Compared with sampling-based approaches including WLMC and QWLMC sampling, QWLMC-ABC localizes rare events at a fraction of the computation time.