Algorithms and Programs of Dynamic Mixture Estimation: Unified Approach to Different Types of Components (SpringerBriefs in Statistics) - Softcover

Nagy, Ivan; Suzdaleva, Evgenia

Softcover

ISBN 10: 3319646702 ISBN 13: 9783319646701

Verlag: Springer, 2017

Inhaltsangabe

Presents and explains the theory of the recursive Bayesian estimation algorithms for dynamic mixture models 
Develops a unified scheme for constructing the estimation algorithm of dynamic mixtures with reproducible statistics
Includes open source programs that can be easily modified or extended by readers

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Über die Autorin bzw. den Autor

Doc. Ing. Ivan Nagy, CSc. (Ph.D.), born 1956 in Prague, Czech Republic, received his CSc. (Ph.D.) in cybernetics from UTIA, Prague in 1983. In 1980, he started working as a researcher at the Institute of Information Theory and Automation of the Czech Academy of Sciences. Since 1998, he has also been a lecturer at the Czech Technical University Faculty of Transportation Sciences in Prague.

Ing. Evgenia Suzdaleva, CSc. (Ph.D.), born 1977 in Krasnoyarsk, Russia, obtained her CSc. (Ph.D.) in 2002 in system analysis at the Siberian State Aerospace University, Krasnoyarsk, Russia. Since 2004, she has been a researcher at the Institute of Information Theory and Automation at the Czech Academy of Sciences. At the same time, she works as a lecturer at the Czech Technical University Faculty of Transportation Sciences in Prague.

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This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2017
• Sprache: Englisch
• ISBN 10: 3319646702
• ISBN 13: 9783319646701
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 128
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Deutschland