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

Nagy, Ivan; Suzdaleva, Evgenia

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