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ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Añadir al carritoPaperback or Softback. Condición: New. Variational Bayesian Learning Theory. Book.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Publicado por Cambridge University Press, Cambridge, 2025
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Añadir al carritoPaperback. Condición: 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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ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Añadir al carritoPaperback. Condición: 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.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Publicado por Cambridge University Press, 2025
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Publicado por Cambridge University Press, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Añadir al carritoPaperback. Condición: 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.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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ISBN 10: 1107430763 ISBN 13: 9781107430761
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Añadir al carritoPaperback. Condición: 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.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
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Añadir al carritoTaschenbuch. Condición: 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.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
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Publicado por Cambridge University Press, GB, 2025
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Añadir al carritoPaperback. Condición: 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.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
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Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
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Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
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Añadir al carritoHardcover. Condición: 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.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
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ISBN 10: 1107076153 ISBN 13: 9781107076150
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