Editore: Uchida Rokakuho Publishing House, Tokyo, 1959
Da: Bibliodditiques, IOBA, Waterloo, ON, Canada
Membro dell'associazione: IOBA
Prima edizione
EUR 11,26
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Very Good +. Kazuho Heida (illustratore). First English language edition. Tight binding. No chips, tears, creases or written inscriptions. Red coverboards with gilt lettering. Beautiful japanes paper endpapers. Light soiling on coverboards. Size: Sm 4to (9" to 11).
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 48,63
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 53,72
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
EUR 62,18
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 65,59
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Chiron Media, Wallingford, Regno Unito
EUR 49,55
Quantità: 1 disponibili
Aggiungi al carrellopaperback. Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 52,50
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 59,51
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Revaluation Books, Exeter, Regno Unito
EUR 73,86
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 559 pages. 6.00x1.25x9.00 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
EUR 63,07
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: Mooney's bookstore, Den Helder, Paesi Bassi
EUR 93,81
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Very good.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Rarewaves.com UK, London, Regno Unito
EUR 59,90
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Editore: Kawade Shobo Shinsha, 1963
Da: Sunny Day Bookstore, SINGAPORE, Singapore
EUR 54,04
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Fine. Number of books: 1.
Editore: Tsukijishokan, 1975
Da: Sunny Day Bookstore, SINGAPORE, Singapore
EUR 54,04
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Fine. Number of books: 1.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 158,23
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 158,22
Quantità: 5 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 176,47
Quantità: 5 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 216,61
Quantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 543 pages. 9.50x6.50x1.25 inches. In Stock.
Editore: Fukui Wakagoshi Publishing Department, 1954
Da: Sunny Day Bookstore, SINGAPORE, Singapore
EUR 62,15
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Fine. Number of books: 1 book.
Editore: Fukui Wakagoshi Publishing Department, 1954
Da: Sunny Day Bookstore, SINGAPORE, Singapore
EUR 62,15
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Fine. Number of books: 1 book.
Editore: Atorie-sha, 1933
Da: Sunny Day Bookstore, SINGAPORE, Singapore
EUR 146,82
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Fine. The book is in fine condition.
Lingua: Inglese
Editore: Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Da: Revaluation Books, Exeter, Regno Unito
EUR 53,45
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 559 pages. 6.00x1.25x9.00 inches. In Stock. This item is printed on demand.
Da: Revaluation Books, Exeter, Regno Unito
EUR 169,30
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 543 pages. 9.50x6.50x1.25 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Da: moluna, Greven, Germania
EUR 164,33
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorShinichi Nakajima is a senior researcher at Technische Universitaet Berlin. His research interests include the theory and applications of machine learning, and he has published papers at numerous conferences and in jour.