Probabilistic Graphical Models (PGMs) represent a powerful framework for encoding complex distributions over large sets of variables, capturing uncertainty and dependencies in a structured, interpretable manner. This approach has become foundational in fields ranging from artificial intelligence and machine learning to bioinformatics and natural language processing. Authored by Daphne Koller and Nir Friedman, this work thoroughly explores the principles and techniques underlying PGMs, elucidating their theoretical foundations as well as practical applications.

• Graphical representations simplify complex probabilistic relationships. Using graphs to encode variables and their conditional dependencies allows for clearer understanding and manipulation of...
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