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This book introduces Bayesian reasoning and Gaussian processes into
machine learning applications. Bayesian methods are applied in many
areas, such as game development, decision making, and drug
discovery. It is very effective for machine learning algorithms in
handling missing data and extracting information from small
datasets. Bayesian Reasoning and Gaussian Processes for Machine
Learning Applications uses a statistical background to understand
continuous distributions and how learning can be viewed from a
probabilistic framework. The chapters progress into such machine
learning topics as belief network and Bayesian reinforcement
learning, which is followed by Gaussian process introduction,
classification, regression, covariance, and performance analysis of
Gaussian processes with other models. FEATURES Contains recent
advancements in machine learning Highlights applications of machine
learning algorithms Offers both quantitative and qualitative
research Includes numerous case studies This book is aimed at
graduates, researchers, and professionals in the field of data
science and machine learning.
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