Efficient Sampling from Multimodal Distribution using Differential Evolution Markov Chain with Replica Exchange

DOI
  • Toriyama Naoki
    Graduate School of Science and Technology, Ryukoku University
  • Ono Keiko
    Faculty of Science and Technology, Ryukoku University

Bibliographic Information

Other Title
  • レプリカ交換型差分進化マルコフ連鎖による多峰性分布からの効率的なサンプリング

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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Details 詳細情報について

  • CRID
    1390001288036222592
  • NII Article ID
    130007382889
  • DOI
    10.11394/tjpnsec.9.32
  • ISSN
    21857385
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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