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Variational Bayesian Learning Theory (Hardcover)
Loot Price: R3,882
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Variational Bayesian Learning Theory (Hardcover)
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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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