Description
Bayesian Statistical Methods: With Applications to Machine Learning provides a thorough introduction to Bayesian inference and its applications in modern machine learning. The book covers foundational concepts including probability distributions, prior selection, and posterior inference, progressively building toward advanced topics.
Through practical examples and case studies, readers learn how to apply Bayesian methods to classification, regression, and clustering problems. The text emphasizes the intuitive understanding of Bayesian thinking while maintaining mathematical rigor. It bridges classical statistics and machine learning, demonstrating how Bayesian frameworks offer flexible alternatives to frequentist approaches.
Written by experienced statisticians Brian J. Reich and Sujit K. Ghosh, this Chapman & Hall publication is ideal for graduate students, researchers, and practitioners seeking to deepen their understanding of probabilistic modeling and statistical inference in machine learning contexts.
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