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

0477300 - ÚTIA 2018 RIV CH eng B - Monography
Nagy, Ivan - Suzdaleva, Evgenia
Algorithms and Programs of Dynamic Mixture Estimation. Unified Approach to Different Types of Components.
Cham: Springer, 2017. 113 s. SpringerBriefs in Statistics. ISBN 978-3-319-64670-1
R&D Projects: GA ČR GA15-03564S
Institutional support: RVO:67985556
Keywords : dynamic mixture * recursive mixture estimation * algorithms and programs
OECD category: Statistics and probability
https://link.springer.com/book/10.1007/978-3-319-64671-8

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.
Permanent Link: http://hdl.handle.net/11104/0274040