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- W3110358051 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." @default.
- W3110358051 created "2020-12-07" @default.
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- W3110358051 date "2020-12-01" @default.
- W3110358051 modified "2023-09-27" @default.
- W3110358051 title "A Bayesian Decision-Theoretic Change-Point Detection for i.p.i.d. Sources" @default.
- W3110358051 doi "https://doi.org/10.1587/transfun.2020tap0009" @default.
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