The variational Bayes method in signal processing
This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It rev...
Αποθηκεύτηκε σε:
| Κύριοι συγγραφείς: | , |
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| Μορφή: | Livre numérique |
| Γλώσσα: | Anglais |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2006.
Cham : Springer Nature |
| Σειρά: | Signals and Communication Technology
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| Θέματα: | |
| Διαθέσιμο Online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Σημείωση: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • The Variational Bayes Method in Signal Processing, Texte imprimé, 9783642066900 • The Variational Bayes Method in Signal Processing, Texte imprimé, 9783540815433 • The Variational Bayes Method in Signal Processing, Texte imprimé, 9783540288190 |
| Περίληψη: | This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts. Many of the principles are first illustrated via easy-to-follow scalar decomposition problems. In later chapters, successful applications are found in factor analysis for medical image sequences, mixture model identification and speech reconstruction. Results with simulated and real data are presented in detail. The unique development of an eight-step "VB method", which can be followed in all cases, enables the reader to develop a VB inference algorithm from the ground up, for their own particular signal or image model. |
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| Περιγραφή τεκμηρίου: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 3540288201 (en ligne) 9783540288206 (en ligne) |
| ISSN: | 1860-4870 |
| Πρόσβαση: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |