Probabilistic Graphical Models
Probabilistic Graphical Models (PGMs) are a family of probabilistic models that represent complex probability distributions using graphs. PGMs are used to model uncertainty and dependencies between variables in a system. They are composed of nodes, which represent random variables, and edges, which represent the probabilistic relationships between the variables. PGMs can be used for a variety of tasks, including prediction, classification, and decision-making. They are widely used in machine learning, computer vision, natural language processing, and other fields of artificial intelligence.
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