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  3. Variational Bayesian Learning Theory
  • Cited by 21
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    • Publisher:
      Cambridge University Press
      Publication date:
      June 2019
      July 2019
      ISBN:
      9781139879354
      9781107076150
      9781107430761
      DOI:
      https://doi.org/10.1017/9781139879354
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.9kg, 558 Pages
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.84kg, 560 Pages
      • Subjects:
      Computer Science, Pattern Recognition and Machine Learning, Computational Statistics, Machine Learning and Information Science, Statistics and Probability
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    • Selected: Digital
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    Subjects:
    Computer Science, Pattern Recognition and Machine Learning, Computational Statistics, Machine Learning and Information Science, Statistics and Probability
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    Book 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.

    Reviews

    'This book presents a very thorough and useful explanation of classical (pre deep learning) mean field variational Bayes. It covers basic algorithms, detailed derivations for various models (eg matrix factorization, GLMs, GMMs, HMMs), and advanced theory, including results on sparsity of the VB estimator, and asymptotic  properties (generalization bounds).'

    Kevin Murphy - Research scientist, Google Brain

    'This book is an excellent and comprehensive reference on the topic of Variational Bayes (VB) inference, which is heavily used in probabilistic machine learning. It covers VB theory and algorithms, and gives a detailed exploration of these methods for matrix factorization and extensions. It will be an essential guide for those using and developing VB methods.'

    Chris Williams - University of Edinburgh

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    Contents

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    Contents

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