Distributed evolutionary Monte Carlo for Bayesian computing

Abstract

Sampling from a multimodal and high-dimensional target distribution posits a great challenge in Bayesian analysis. A new Markov chain Monte Carlo algorithm Distributed Evolutionary Monte Carlo (DGMC) is proposed for real-valued problems, which combines the attractive features of the distributed genetic algorithm and the Markov chain Monte Carlo. The DGMC algorithm evolves a population of Markov chains through some genetic operators to simulate the target function. Theoretical justification proves that the DGMC algorithm has the target function as its stationary distribution. The effectiveness of the DGMC algorithm is illustrated by simulating two multimodal distributions and an application to a real data example.