Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Speedyhen, London, Reino Unido
EUR 45,60
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: NEW.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 54,90
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 52,85
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press 2/6/2025, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
EUR 51,48
Convertir monedaCantidad disponible: 5 disponibles
Añadir al carritoPaperback or Softback. Condición: New. Variational Bayesian Learning Theory 1.79. Book.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 61,07
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2025. paperback. . . . . .
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 53,91
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: Brand New. 559 pages. 6.00x1.25x9.00 inches. In Stock.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 49,11
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 58,28
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Majestic Books, Hounslow, Reino Unido
EUR 58,17
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 55,51
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 54,89
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 59,76
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 74,77
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2025. paperback. . . . . . Books ship from the US and Ireland.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 59,76
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 73,24
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 559 pages. 6.00x1.25x9.00 inches. In Stock.
Publicado por Cambridge University Press, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 56,55
Convertir monedaCantidad disponible: 1 disponibles
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, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 78,94
Convertir monedaCantidad disponible: 1 disponibles
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, Cambridge, 2025
ISBN 10: 1107430763 ISBN 13: 9781107430761
Idioma: Inglés
Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
EUR 57,87
Convertir monedaCantidad disponible: 1 disponibles
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.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 134,64
Convertir monedaCantidad disponible: 5 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: Mooney's bookstore, Den Helder, Holanda
EUR 140,98
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoCondición: Very good.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 143,48
Convertir monedaCantidad disponible: 5 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 144,26
Convertir monedaCantidad disponible: 5 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 161,22
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 165,03
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 147,75
Convertir monedaCantidad disponible: 1 disponibles
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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 165,02
Convertir monedaCantidad disponible: 5 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press CUP, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 188,71
Convertir monedaCantidad disponible: 4 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
EUR 143,06
Convertir monedaCantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Publicado por Cambridge University Press, Cambridge, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 173,87
Convertir monedaCantidad disponible: 1 disponibles
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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por Cambridge University Press, 2019
ISBN 10: 1107076153 ISBN 13: 9781107076150
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 210,55
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.