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Probabilistic Graphical Models (PGMs) are a rich framework for encoding probability distributions over complex domains using graphs to represent the conditional dependencies between random variables. They combine the strengths of probability theory and graph theory to provide a compact and intuitive representation of joint probability distributions, enabling efficient inference and learning in large-scale systems.
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