Graphical Models

Graphical models are a set of statistical models that use graphs to represent and visualize the relationships between variables. These models are used to analyze complex systems and make predictions based on the relationships between variables. Graphical models are widely used in machine learning, computer vision, natural language processing, and other areas of artificial intelligence. They are particularly useful for modeling systems with many variables and complex dependencies. There are two main types of graphical models: Bayesian networks and Markov random fields. Bayesian networks are directed acyclic graphs that represent the conditional dependencies between variables. Markov random fields are undirected graphs that represent the joint probability distribution over a set of variables. Graphical models can be used for a variety of tasks, including classification, regression, clustering, and anomaly detection.
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