國立虎尾科技大學 |

Variational Bayesian learning theory /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Variational Bayesian learning theory // Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama.
Author:	Nakajima, Shin'ichi,
other author:	Watanabe, Kazuho,
Description:	1 online resource (xv, 543 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 28 Jun 2019).
Subject:	Bayesian field theory. -
Online resource:	https://doi.org/10.1017/9781139879354
ISBN:	9781139879354 (ebook)

Variational Bayesian learning theory /
Nakajima, Shin'ichi,

Variational Bayesian learning theory /Shinichi Nakajima, Kazuho Watanabe, Masashi Sugiyama. - 1 online resource (xv, 543 pages) :digital, PDF file(s).

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: 9781139879354 (ebook)Subjects--Topical Terms:

970512
Bayesian field theory.

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

Dewey Class. No.: 519.2/33

Variational Bayesian learning theory /
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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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