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|a 9783319646718
|9 978-3-319-64671-8
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|a 10.1007/978-3-319-64671-8
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|a Nagy, Ivan.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Algorithms and Programs of Dynamic Mixture Estimation
|h [electronic resource] :
|b Unified Approach to Different Types of Components /
|c by Ivan Nagy, Evgenia Suzdaleva.
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| 250 |
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|a 1st ed. 2017.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XI, 113 p. 27 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
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|a SpringerBriefs in Statistics,
|x 2191-544X
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| 505 |
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|a Introduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices.
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| 520 |
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|a 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.
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|a Probabilities.
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|a Statistics .
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|a System theory.
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|a Computer simulation.
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|a Algorithms.
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| 650 |
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|a Probability Theory and Stochastic Processes.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M27004
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|a Statistical Theory and Methods.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/S11001
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|a Systems Theory, Control.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M13070
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|a Simulation and Modeling.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I19000
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|a Algorithms.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M14018
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| 700 |
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|a Suzdaleva, Evgenia.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783319646701
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| 776 |
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|i Printed edition:
|z 9783319646725
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| 950 |
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|a Mathematics and Statistics (R0) (SpringerNature-43713)
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