Tayal H.K.S., George P.M., Singla P., Kose U. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Boca Raton: CRC Press/Chapman & Hall, 2022. — 149 p.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 use 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 networks and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models.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.