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Variational bayesian learning theory

By: Contributor(s): Material type: TextTextPublication details: Cambridge University Press 2019 CambridgeDescription: xv, 543 p. : ill. Includes bibliographical reference & indexISBN:
  • 9781107076150
Subject(s): DDC classification:
  • 519.233 N2V2
Summary: 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. https://www.cambridge.org/core/books/variational-bayesian-learning-theory/0F6AABA050630E01E1B6EDA5E2CAFA05#fndtn-information
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Item type Current library Item location Collection Shelving location Call number Status Notes Date due Barcode
Books Vikram Sarabhai Library Rack 28-B / Slot 1403 (0 Floor, East Wing) Non-fiction General Stacks 519.233 N2V2 (Browse shelf(Opens below)) Available 207093

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.


https://www.cambridge.org/core/books/variational-bayesian-learning-theory/0F6AABA050630E01E1B6EDA5E2CAFA05#fndtn-information

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