Transaction of the Japanese Society for Evolutionary Computation
Online ISSN : 2185-7385
ISSN-L : 2185-7385
Original Paper : Special Issue of the 2017 Symposium on Evolutionary Computation
Efficient Sampling from Multimodal Distribution using Differential Evolution Markov Chain with Replica Exchange
Naoki ToriyamaKeiko Ono
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2018 Volume 9 Issue 2 Pages 32-40

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Abstract
In this paper, we present an efficient sampling method for a multimodal and high-dimensional distribution. For sampling from a high-dimensional distribution, DE-MC, which is based on the Markov chain Monte Carlo(MCMC) methods, has been proposed. It showed good performance in sampling from any probability distribution based on constructing a Markov chain that has the desired distribution. However, DE-MC has inherent difficulties in sampling from a multimodal distribution. To overcome this problem, we incorporate a replica exchange method into DE-MC and propose a replica exchange resampling DE-MC method (reRDE-MC) based on sampling importance resampling to improve its performance. The proposed method is evaluated by using three types of distributions with multimodal and high dimensions as artificial data. We verified that the proposed method can sample from a multimodal and highdimensional distribution more effectively than by a conventional method. We then evaluated the proposed method by using financial data as actual data, and confirmed that the proposed method can capture the behavior of financial data.
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© 2018 The Japanese Society for Evolutionary Computation
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