Algorithms and Programs of Dynamic Mixture Estimation: Unified Approach to Different Types of Components

✍ Scribed by Ivan Nagy, Evgenia Suzdaleva

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 116
• Series: SpringerBriefs in Statistics
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Front Matter ....Pages i-ix
Introduction (Ivan Nagy, Evgenia Suzdaleva)....Pages 1-7
Basic Models (Ivan Nagy, Evgenia Suzdaleva)....Pages 9-17
Statistical Analysis of Dynamic Mixtures (Ivan Nagy, Evgenia Suzdaleva)....Pages 19-27
Dynamic Mixture Estimation (Ivan Nagy, Evgenia Suzdaleva)....Pages 29-43
Program Codes (Ivan Nagy, Evgenia Suzdaleva)....Pages 45-55
Experiments (Ivan Nagy, Evgenia Suzdaleva)....Pages 57-84
Appendix A (Supporting Notions) (Ivan Nagy, Evgenia Suzdaleva)....Pages 85-97
Appendix B (Supporting Programs) (Ivan Nagy, Evgenia Suzdaleva)....Pages 99-110
Back Matter ....Pages 111-113

✦ Subjects

Estimation theory;Regression analysis -- Mathematical models