Population-based Monte Carlo

Abstract

Population-based Monte Carlo simulations can be used to extend Monte Carlo (MC) methods for solving complex multimodal posterior distributions that can arise in the context of inference over a multimodal distribution using an interacting particles system. This paper presents preliminary results that shows a population-based Monte Carlo strategy can help the large number of samples required for performing inference with the standard implementations of MC methods, while introducing better adaptivity and exploring cappabilities.