Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
by Daphne Koller, Nir Friedman
A comprehensive guide to probabilistic graphical models, detailing principles and techniques for modeling uncertainty and supporting inference in machine learning.
About This Book
Probabilistic Graphical Models presents core principles and advanced techniques for representing and reasoning with uncertainty.
The book explores graphical structures that encode probabilistic relationships among variables in complex domains.
Readers gain insight into inference, learning, and decision-making methods grounded in probabilistic frameworks.
Content is designed for graduate students and researchers working in machine learning and related fields.
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