Bayesian Reasoning And Gaussian Processes For Machine Learning Applications


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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications


Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Author: Hemachandran K

language: en

Publisher: CRC Press

Release Date: 2022-04-14


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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.

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications


Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Author: Hemachandran K

language: en

Publisher: CRC Press

Release Date: 2022-04-19


DOWNLOAD





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.

Bayesian Reasoning and Machine Learning


Bayesian Reasoning and Machine Learning

Author: David Barber

language: en

Publisher: Cambridge University Press

Release Date: 2012-02-02


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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.


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