Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling

Vesselin P. Jilkov; X. Rong Li; Donka S. Angelova

doi:10.1007/3-540-36487-0_34

Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling

Vesselin P. Jilkov
, X. Rong Li
, Donka S. Angelova

University of New Orleans
Bulgarian Academy of Sciences

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

12 引用（Scopus）

摘要

Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.

源语言	英语
主期刊名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编辑	Ivan Dimov, Ivan Lirkov, Svetozar Margenov, Zahari Zlatev
出版商	Springer Verlag
页	307-315
页数	9
ISBN（印刷版）	3540006087, 9783540006084
DOI	https://doi.org/10.1007/3-540-36487-0_34
出版状态	已出版 - 2003
已对外发布	是

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	2542
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

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10.1007/3-540-36487-0_34

其它文件与链接

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引用此

Jilkov, V. P., Li, X. R., & Angelova, D. S. (2003). Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling. 在 I. Dimov, I. Lirkov, S. Margenov, & Z. Zlatev (编辑), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (页码 307-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 2542). Springer Verlag. https://doi.org/10.1007/3-540-36487-0_34

Jilkov, Vesselin P. ; Li, X. Rong ; Angelova, Donka S. / Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 编辑 / Ivan Dimov ; Ivan Lirkov ; Svetozar Margenov ; Zahari Zlatev. Springer Verlag, 2003. 页码 307-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling",

abstract = "Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.",

author = "Jilkov, \{Vesselin P.\} and Li, \{X. Rong\} and Angelova, \{Donka S.\}",

year = "2003",

doi = "10.1007/3-540-36487-0\_34",

language = "英语",

isbn = "3540006087",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "307--315",

editor = "Ivan Dimov and Ivan Lirkov and Svetozar Margenov and Zahari Zlatev",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

Jilkov, VP, Li, XR & Angelova, DS 2003, Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling. 在 I Dimov, I Lirkov, S Margenov & Z Zlatev (编辑), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 2542, Springer Verlag, 页码 307-315. https://doi.org/10.1007/3-540-36487-0_34

Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling. / Jilkov, Vesselin P.; Li, X. Rong; Angelova, Donka S.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 编辑 / Ivan Dimov; Ivan Lirkov; Svetozar Margenov; Zahari Zlatev. Springer Verlag, 2003. 页码 307-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 2542).

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

TY - CHAP

T1 - Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling

AU - Jilkov, Vesselin P.

AU - Li, X. Rong

AU - Angelova, Donka S.

PY - 2003

Y1 - 2003

N2 - Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.

AB - Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.

UR - https://www.scopus.com/pages/publications/29844455355

U2 - 10.1007/3-540-36487-0_34

DO - 10.1007/3-540-36487-0_34

M3 - 章节

AN - SCOPUS:29844455355

SN - 3540006087

SN - 9783540006084

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 307

EP - 315

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Dimov, Ivan

A2 - Lirkov, Ivan

A2 - Margenov, Svetozar

A2 - Zlatev, Zahari

PB - Springer Verlag

ER -

Jilkov VP, Li XR, Angelova DS. Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling. 在 Dimov I, Lirkov I, Margenov S, Zlatev Z, 编辑, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. 页码 307-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-36487-0_34

Estimation of Markovian jump systems with unknown transition probabilities through Bayesian sampling

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