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Variational Bayesian learning theory

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Variational Bayesian learning theory/ Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama.
作者:	Nakajima, Shin'ichi.
其他作者:	Sugiyama, Masashi.
出版者:	Cambridge :Cambridge University Press, : 2019.,
面頁冊數:	xv, 543 p. :ill., digital ; : 24 cm.;
附註:	Title from publisher's bibliographic system (viewed on 28 Jun 2019).
標題:	Probabilities. -
電子資源:	https://doi.org/10.1017/9781139879354
ISBN:	9781139879354

Variational Bayesian learning theory
Nakajima, Shin'ichi.

Variational Bayesian learning theory[electronic resource] /Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama. - Cambridge :Cambridge University Press,2019. - xv, 543 p. :ill., digital ;24 cm.

Title from publisher's bibliographic system (viewed on 28 Jun 2019).

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.

ISBN: 9781139879354Subjects--Topical Terms:

527847
Probabilities.

LC Class. No.: QC174.85.B38 / N35 2019

Dewey Class. No.: 519.233

Variational Bayesian learning theory
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500 $a Title from publisher's bibliographic system (viewed on 28 Jun 2019).
520 $a 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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