Probabilistic graphical models (PGMs) are a type of statistical model that uses a graph to represent the probabilistic relationships between random variables. They are commonly used for reasoning under uncertainty, prediction, and knowledge discovery in fields like machine learning, computer vision, natural language processing, and bioinformatics. They allow for efficient representation and computation of complex probability distributions.
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