Variational Bayesian Learning Theory

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Variational Bayesian Learning Theory
Format:
Bog, hardback
Udgivelsesdato:
11-07-2019
Sprog:
Engelsk
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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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Udgivelsesdato:
11-07-2019
ISBN13:
9781107076150
Vægt:
900 g
Dybde:
34 mm
Bredde:
156 mm
Højde:
235 mm
Format:
Hardback
  • Forfattere

  • Bibliotekernes beskrivelse

    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.

Vis mereVis mindre

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Variational Bayesian Learning Theory

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  • Leveringstid 2-3 hverdage
  • Forventet levering 23-10-2019

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