Details
Algorithms and Programs of Dynamic Mixture Estimation
Unified Approach to Different Types of ComponentsSpringerBriefs in Statistics
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53,49 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 14.08.2017 |
| ISBN/EAN: | 9783319646718 |
| Sprache: | englisch |
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Beschreibungen
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.
Introduction.- Basic Models.- Statistical Analysis of Dynamic Mixtures.- Dynamic Mixture Estimation.- Program Codes.- Experiments.- Appendices.
<div>Doc. Ing. <b>Ivan Nagy</b>, 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.<br></div><div><br></div><div>Ing. <b>Evgenia Suzdaleva</b>, 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.<br></div>
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.<br>
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 Includes supplementary material: sn.pub/extras
Presents and explains the theory of the recursive Bayesian estimation algorithms for dynamic mixture models <br>Develops a unified scheme for constructing the estimation algorithm of dynamic mixtures with reproducible statistics<br>Includes open source programs that can be easily modified or extended by readers<br>
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