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

Algorithms and Programs of Dynamic Mixture Estimation: Unified Approach to Different Types of Components /
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Field name Details
Dewey Class 519.2
Title Algorithms and Programs of Dynamic Mixture Estimation ([EBook]) : Unified Approach to Different Types of Components / / by Ivan Nagy, Evgenia Suzdaleva.
Author Nagy, Ivan
Added Personal Name Suzdaleva, Evgenia author.
Other name(s) SpringerLink (Online service)
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2017.
Physical Details XI, 113 p. 27 illus. in color. : online resource.
Series SpringerBriefs in Statistics 2191-544X
ISBN 9783319646718
Summary Note 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.:
Contents note Introduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site http://dx.doi.org/10.1007/978-3-319-64671-8
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