Distributed Evolutionary Monte Carlo with Applications to Bayesian Analysis
نویسندگان
چکیده
Sampling from multimodal and high dimensional target distribution posits a great challenge in Bayesian analysis. This paper combines the attractive features of the distributed genetic algorithm and the Markov Chain Monte Carlo, resulting in a new Monte Carlo algorithm Distributed Evolutionary Monte Carlo (DEMC) for real-valued problems. DEMC evolves a population of the Markov chains through genetic operators to explore the target function efficiently. The promising potential of the DEMC algorithm is illustrated by applying it to multimodal samples, Bayesian Neural Network and logistic regression inference.
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