Using of CUSUM change-point detection algorithm for Markov-modulated Poisson process identification

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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/291864

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DC Field	Value	Language
dc.contributor.author	Vorobejchikov, S. E.
dc.contributor.author	Burkatovskaya, Yu. B.
dc.date.accessioned	2023-01-13T09:38:37Z	-
dc.date.available	2023-01-13T09:38:37Z	-
dc.date.issued	2022
dc.identifier.citation	Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the XIII Intern. Conf., Minsk, Sept. 6–10, 2022 / Belarusian State University ; eds.: Yu. Kharin [et al.]. – Minsk : BSU, 2022. – Pp. 218-221.
dc.identifier.isbn	978-985-881-420-5
dc.identifier.uri	https://elib.bsu.by/handle/123456789/291864	-
dc.description.abstract	The paper is devoted to the problem of Markov-modulated Poisson process identification. The length of the intervals between neighboring arrivals are considered as a discrete-time continuous-valued process with multiple change-points. A CUSUM algorithm to detect these change-points and, hence, the changes of the hidden Markovian chain state is developed
dc.language.iso	en
dc.publisher	Minsk : BSU
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.title	Using of CUSUM change-point detection algorithm for Markov-modulated Poisson process identification
dc.type	conference paper
Appears in Collections:	2022. Computer Data Analysis and Modeling: Stochastics and Data Science

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