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Variational Bayesian Learning Theory
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Variational Bayesian Learning Theory

by Kazuho Watanabe , Masashi Sugiyama , Shinichi Nakajima

Book Details

Format Paperback / Softback
ISBN-10 1107430763
ISBN-13 9781107430761
Publisher Cambridge University Press
Imprint Cambridge University Press
Country of Manufacture GB
Country of Publication GB
Publication Date Feb 6th, 2025
Print length 559 Pages
Weight 836 grams
Dimensions 15.10 x 23.00 x 4.00 cms
Product Classification: Bayesian inference Science: general issues Machine learning
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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.
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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