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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory (Paperback or Softback)
Published by Cambridge University Press 2/6/2025, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
Paperback or Softback. Condition: New. Variational Bayesian Learning Theory 1.79. Book.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory (Paperback)
Published by Cambridge University Press, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
Paperback. Condition: new. Paperback. 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. Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Variational Bayesian Learning Theory
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Variational Bayesian Learning Theory
Published by Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Add to basketPaperback. Condition: 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.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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VARIATIONAL BAYESIAN LEARNING THEORY
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Variational Bayesian Learning Theory (Paperback)
Published by Cambridge University Press, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
US$ 65.62
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Add to basketPaperback. Condition: new. Paperback. 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. Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Add to basketPaperback. Condition: Brand New. 559 pages. 6.00x1.25x9.00 inches. In Stock.
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Variational Bayesian Learning Theory (Paperback)
Published by Cambridge University Press, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
US$ 93.65
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. 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. Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Condition: New.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Condition: New. SUPER FAST SHIPPING.
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Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
US$ 159.72
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Variational Bayesian Learning Theory
Published by Cambridge University Press, GB, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Language: English
US$ 79.68
Convert currencyUS$ 88.12 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPaperback. Condition: 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.
-
Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Condition: New.
-
Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Condition: As New. Unread book in perfect condition.
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Variational Bayesian Learning Theory (Hardcover)
Published by Cambridge University Press, Cambridge, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Hardcover. Condition: new. Hardcover. 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. Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggests how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
-
Variational Bayesian Learning Theory
Published by Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 191.51
Convert currencyUS$ 16.24 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
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