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Ajouter au panierPaperback. Etat : Brand New. 113 pages. 9.00x6.00x0.25 inches. In Stock.
Langue: anglais
Edité par Springer, Berlin, Springer, 2017
ISBN 10 : 3319646702 ISBN 13 : 9783319646701
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Ajouter au panierTaschenbuch. Etat : Neu. Algorithms and Programs of Dynamic Mixture Estimation | Unified Approach to Different Types of Components | Ivan Nagy (u. a.) | Taschenbuch | xi | Englisch | 2017 | Springer | EAN 9783319646701 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Langue: anglais
Edité par Berlin Springer International Publishing Springer Aug 2017, 2017
ISBN 10 : 3319646702 ISBN 13 : 9783319646701
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 113 pp. Englisch.
Langue: anglais
Edité par Springer International Publishing, 2017
ISBN 10 : 3319646702 ISBN 13 : 9783319646701
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierKartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents and explains the theory of the recursive Bayesian estimation algorithms for dynamic mixture models Develops a unified scheme for constructing the estimation algorithm of dynamic mixtures with reproducible statisticsIncludes open source.