A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources

• SUZUKI Kairi

  Department of Pure and Applied Mathematics, Graduate School of Fundamental Science and Engineering, Waseda University

• KAMATSUKA Akira

  Global Education Center (GEC), Waseda University

• MATSUSHIMA Toshiyasu

  Department of Pure and Applied Mathematics, Graduate School of Fundamental Science and Engineering, Waseda University

抄録

Change-point detection is the problem of finding points of time when a probability distribution of samples changed. There are various related problems, such as estimating the number of the change-points and estimating magnitude of the change. Though various statistical models have been assumed in the field of change-point detection, we particularly deal with i.p.i.d. (independent-piecewise-identically-distributed) sources. In this paper, we formulate the related problems in a general manner based on statistical decision theory. Then we derive optimal estimators for the problems under the Bayes risk principle. We also propose efficient algorithms for the change-point detection-related problems in the i.p.i.d. sources, while in general, the optimal estimations requires huge amount of calculation in Bayesian setting. Comparison of the proposed algorithm and previous methods are made through numerical examples.

収録刊行物

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A (12), 1393-1402, 2020-12-01

  一般社団法人 電子情報通信学会

キーワード

• change-point detection
• statistical decision theory
• Bayes risk principle
• i.p.i.d. sources

詳細情報

• CRID: 1391130851441935488
• NII論文ID: 130007948300
• DOI: 10.1587/transfun.2020tap0009
• ISSN: 17451337
• ISSN: 09168508
• Web Site: https://www.jstage.jst.go.jp/article/transfun/E103.A/12/E103.A_2020TAP0009/_pdf
• 本文言語コード: en
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