Articles liés à Multivariate Bayesian Statistics
Synopsis
Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the "cocktail-party" analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters. Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many "cocktail party" problems they may confront in practice.
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À propos de l?auteur
Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
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Multivariate Bayesian Statistics
Edité par
Taylor & Francis, Chapman And Hall/CRC, 2019
ISBN 10 : 0367454661
ISBN 13 : 9780367454661
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the 'cocktail-party' analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters.Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many 'cocktail party' problems they may confront in practice. 350 pp. Englisch. N° de réf. du vendeur 9780367454661
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Edité par
Taylor & Francis Ltd, 2019
ISBN 10 : 0367454661
ISBN 13 : 9780367454661
Neuf
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days. 185. N° de réf. du vendeur B9780367454661
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Rowe, D: Multivariate Bayesian Statistics
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.Of the two primary approaches to the classic source separation problem, only one doe. N° de réf. du vendeur 594583341
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 352 pages. 9.21x6.14x0.79 inches. In Stock. N° de réf. du vendeur __0367454661
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Edité par
Chapman and Hall/CRC, 2019
ISBN 10 : 0367454661
ISBN 13 : 9780367454661
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. pp. 352. N° de réf. du vendeur 380159410
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Edité par
Chapman and Hall/CRC, 2019
ISBN 10 : 0367454661
ISBN 13 : 9780367454661
Neuf
Couverture souple
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. pp. 352. N° de réf. du vendeur 18383744615
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Edité par
Chapman and Hall/CRC, 2019
ISBN 10 : 0367454661
ISBN 13 : 9780367454661
Neuf
Couverture souple
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 352. N° de réf. du vendeur 26383744621
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Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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