Algorithms and Programs of Dynamic Mixture Estimation

Unified Approach to Different Types of Components

Ivan Nagy, Evgenia Suzdaleva (Autoren)

Buch | Softcover

XI, 113 Seiten

2017 | 1st ed. 2017
Springer International Publishing (Verlag)
978-3-319-64670-1 (ISBN)

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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.

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.

Introduction.- Basic Models.- Statistical Analysis of Dynamic Mixtures.- Dynamic Mixture Estimation.- Program Codes.- Experiments.- Appendices.

"The book presents and discusses dynamic mixture models and their use in estimation and prediction. ... Mixture models have applications in several domains such as industry, engineering, social science, medicine, transportation etc. The book therefore can be of interest to researchers and PhD students in many diverse fields." (Christina Diakaki, zbMATH 1383.62005, 2018)

“The book presents and discusses dynamic mixture models and their use in estimation and prediction. ... Mixture models have applications in several domains such as industry, engineering, social science, medicine, transportation etc. The book therefore can be of interest to researchers and PhD students in many diverse fields.” (Christina Diakaki, zbMATH 1383.62005, 2018)

Erscheinungsdatum	06.09.2017
Reihe/Serie	SpringerBriefs in Statistics
Zusatzinfo	XI, 113 p. 27 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	200 g
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Schlagworte	3D graphics & modelling • 3D graphics & modelling • algorithms • cybernetics & systems theory • Cybernetics & systems theory • dynamic mixtures • Markov Switching Models • Mathematics • mathematics and statistics • mixture estimation algorithms • Mixture Models • mixture prediction • mixtures of various distributions • Numerical analysis • open source programs • probability & statistics • Probability & statistics • Probability theory and stochastic processes • Recursive Bayesian estimation • Simulation and modeling • Statistical Theory and Methods • stochastics • Systems Theory, Control
ISBN-10	3-319-64670-2 / 3319646702
ISBN-13	978-3-319-64670-1 / 9783319646701
Zustand	Neuware