A bayesian decision-theoretic change-point detection for i.p.i.d. sources

Kairi Suzuki, Akira Kamatsuka, Toshiyasu Matsushima

研究成果: Article査読

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 changepoints 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 e_cient 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.

本文言語English
ページ(範囲)1393-1402
ページ数10
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E103A
12
DOI
出版ステータスPublished - 2020 12

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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