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Algorithms and Programs of Dynamic Mixture Estimation

Unified Approach to Different Types of Components

  • Book
  • © 2017

Overview

Authors:
  1. Ivan Nagy

    1. Department of Signal Processing, Institute of Information Theory and Automation of the Czech Academy of Sciences and Czech Technical University in Prague, Prague, Czech Republic

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  2. Evgenia Suzdaleva

    1. Department of Signal Processing, Institute of Information Theory and Automation of the Czech Academy of Sciences, Prague, Czech Republic

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    You can also search for this author in PubMed   Google Scholar

  • 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)

  1. Front Matter

    Pages i-xi
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  2. Introduction

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 1-7

  3. Basic Models

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 9-17

  4. Statistical Analysis of Dynamic Mixtures

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 19-27

  5. Dynamic Mixture Estimation

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 29-43

  6. Program Codes

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 45-55

  7. Experiments

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 57-84

  8. Appendix A (Supporting Notions)

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 85-97

  9. Appendix B (Supporting Programs)

    • Ivan Nagy, Evgenia Suzdaleva
    Pages 99-110

  10. Erratum to: Algorithms and Programs of Dynamic Mixture Estimation

    • Ivan Nagy, Evgenia Suzdaleva
    Pages E1-E1

  11. Back Matter

    Pages 111-113
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Keywords

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.

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)

Authors and Affiliations

  • Department of Signal Processing, Institute of Information Theory and Automation of the Czech Academy of Sciences and Czech Technical University in Prague, Prague, Czech Republic

    Ivan Nagy

  • Department of Signal Processing, Institute of Information Theory and Automation of the Czech Academy of Sciences, Prague, Czech Republic

    Evgenia Suzdaleva

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.

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