SpringerBriefs in Statistics

Algorithms and Programs of Dynamic Mixture Estimation

Unified Approach to Different Types of Components

Authors: Nagy, Ivan, Suzdaleva, Evgenia

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  • 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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eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-64671-8
  • Digitally watermarked, DRM-free
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  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-64670-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

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.

Reviews

“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)

Table of contents (9 chapters)

Buy this book

eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-64671-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-64670-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Algorithms and Programs of Dynamic Mixture Estimation
Book Subtitle
Unified Approach to Different Types of Components
Authors
Series Title
SpringerBriefs in Statistics
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-64671-8
DOI
10.1007/978-3-319-64671-8
Softcover ISBN
978-3-319-64670-1
Series ISSN
2191-544X
Edition Number
1
Number of Pages
XI, 113
Number of Illustrations
27 illustrations in colour
Topics