Probabilistic Graphical Model
Probabilistic Graphical Model (PGM) is a type of statistical model that represents complex probabilistic relationships between random variables through a graph. PGMs are used to model uncertainty and make predictions in various fields such as computer vision, natural language processing, bioinformatics, and social network analysis. PGMs consist of nodes that represent random variables and edges that represent probabilistic dependencies between them. There are two main types of PGMs: Bayesian Networks and Markov Networks. Bayesian Networks are directed acyclic graphs that represent causal relationships between variables, while Markov Networks are undirected graphs that represent conditional independence relationships between variables. PGMs can be used for various tasks such as classification, regression, clustering, and anomaly detection.
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