Description: 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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EAN: 9781107076150
UPC: 9781107076150
ISBN: 9781107076150
MPN: N/A
Book Title: Variational Bayesian Learning Theory by Shinichi
Item Length: 21.3 cm
Number of Pages: 558 Pages
Language: English
Publication Name: Variational Bayesian Learning Theory
Publisher: Cambridge University Press
Publication Year: 2019
Subject: Computer Science, Science, Mathematics
Item Height: 235 mm
Item Weight: 900 g
Type: Textbook
Author: Masashi Sugiyama, Kazuho Watanabe, Shinichi Nakajima
Item Width: 156 mm
Format: Hardcover
