Probabilistic Graphical Models
Probabilistic Graphical Models (PGMs) are a class of statistical models that use a graph-based representation to encode the complex probabilistic relationships among a set of random variables. Within this framework, nodes represent the variables and edges signify conditional dependencies, allowing for a compact and intuitive visualization of a complex joint probability distribution. By merging graph theory with probability theory, PGMs provide a powerful system for reasoning and performing inference under uncertainty, with key examples including Bayesian Networks (using directed graphs) and Markov Random Fields (using undirected graphs).
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