Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
by Daphne Koller, Nir Friedman
Daphne Koller and Nir Friedman offer a comprehensive study of probabilistic graphical models, emphasizing the principles and techniques used to represent and reason about uncertainty. Part of the Adaptive Computation and Machine Learning series, this reference is designed for readers exploring the foundations of modern machine learning.
About This Book
Probabilistic Graphical Models: Principles and Techniques presents a focused study of graphical models and probabilistic reasoning.
Written by Daphne Koller and Nir Friedman, the book examines principles and techniques within the context of adaptive computation and machine learning.
It is suited to readers seeking a rigorous reference on the mathematical and computational ideas underlying structured probabilistic models.
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