Overview
- 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
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
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Table of contents (9 chapters)
Keywords
About this book
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Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Algorithms and Programs of Dynamic Mixture Estimation
Book Subtitle: Unified Approach to Different Types of Components
Authors: Ivan Nagy, Evgenia Suzdaleva
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-319-64671-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-64670-1Published: 24 August 2017
eBook ISBN: 978-3-319-64671-8Published: 14 August 2017
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: XI, 113
Number of Illustrations: 27 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods, Systems Theory, Control, Simulation and Modeling, Algorithms