Probabilistic Graphical Models: Principles and Techniques - Hardcover
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Comprehensive introduction to probabilistic graphical models for beginners and advanced readers
Detailed exploration of Bayesian networks and Markov networks for modeling dependencies
Applications in machine learning, computer vision, NLP, and bioinformatics for real-world relevance
Case studies and examples illustrate practical problem-solving using PGMS
Hardcover format for durability and professional presentation
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Foundations of Probabilistic Graphical Models: Provide a comprehensive introduction to probabilistic graphical models (PGMs), including their purpose, fundamental concepts, and types (e.g., Bayesian networks, Markov networks). Discuss the representation of complex probability distrib