IEICE TRANSACTIONS on Fundamentals
A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.12 pp.1393-1402
- Publication Date
- 2020/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020TAP0009
- Type of Manuscript
- Special Section PAPER (Special Section on Information Theory and Its Applications)
- Category
- Machine Learning
Authors
Kairi SUZUKI
Waseda University
Akira KAMATSUKA
Waseda University
Toshiyasu MATSUSHIMA
Waseda University
Keyword
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Kairi SUZUKI, Akira KAMATSUKA, Toshiyasu MATSUSHIMA, "A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1393-1402, December 2020, doi: 10.1587/transfun.2020TAP0009.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020TAP0009/_p
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@ARTICLE{e103-a_12_1393,
author={Kairi SUZUKI, Akira KAMATSUKA, Toshiyasu MATSUSHIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources},
year={2020},
volume={E103-A},
number={12},
pages={1393-1402},
abstract={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.},
keywords={},
doi={10.1587/transfun.2020TAP0009},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1393
EP - 1402
AU - Kairi SUZUKI
AU - Akira KAMATSUKA
AU - Toshiyasu MATSUSHIMA
PY - 2020
DO - 10.1587/transfun.2020TAP0009
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - 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.
ER -
English
