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“Graphical models” books focus on probabilistic graphical models—frameworks that use graphs to represent complex distributions and the conditional dependencies among random variables. Titles in this category typically cover core formalisms such as Bayesian networks (directed), Markov random fields (undirected), and factor graphs; foundational concepts like conditional independence and d-separation; and the main tasks of representation, inference, and learning. Readers will find treatments of exact and approximate inference algorithms (e.g., variable elimination, junction tree, belief propagation, variational methods, MCMC), parameter estimation, structure learning, and sometimes causal inference and decision-making with influence diagrams. The category spans theory and practice, from introductory textbooks to advanced monographs and applied guides with code, case studies, and exercises. Applications commonly include machine learning, statistics, natural language processing, computer vision, bioinformatics, recommendation systems, and time-series modeling.

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