Advertisement

Algorithms and Programs of Dynamic Mixture Estimation

Unified Approach to Different Types of Components

  • Ivan Nagy
  • Evgenia Suzdaleva

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ivan Nagy, Evgenia Suzdaleva
    Pages 1-7
  3. Ivan Nagy, Evgenia Suzdaleva
    Pages 9-17
  4. Ivan Nagy, Evgenia Suzdaleva
    Pages 19-27
  5. Ivan Nagy, Evgenia Suzdaleva
    Pages 29-43
  6. Ivan Nagy, Evgenia Suzdaleva
    Pages 45-55
  7. Ivan Nagy, Evgenia Suzdaleva
    Pages 57-84
  8. Ivan Nagy, Evgenia Suzdaleva
    Pages 85-97
  9. Ivan Nagy, Evgenia Suzdaleva
    Pages 99-110
  10. Back Matter
    Pages 111-113

About this book

Introduction

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.

Keywords

mixture models Markov switching models dynamic mixtures recursive Bayesian estimation mixture estimation algorithms mixtures of various distributions open source programs mixture prediction

Authors and affiliations

  • Ivan Nagy
    • 1
  • Evgenia Suzdaleva
    • 2
  1. 1.Department of Signal ProcessingInstitute of Information Theory and Automation of the Czech Academy of Sciences and Czech Technical University in PraguePragueCzech Republic
  2. 2.Department of Signal ProcessingInstitute of Information Theory and Automation of the Czech Academy of SciencesPragueCzech Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-64671-8
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-64670-1
  • Online ISBN 978-3-319-64671-8
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site