Graphical model: Difference between revisions

Revision as of 21:07, 27 April 2007

Revision as of 22:18, 27 April 2007

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In other words, the [[probability distribution|joint distribution]] factors into a product of conditional distributions. The graph structure indicates direct dependencies among random variables. Any two nodes that are not in a descendant/ancestor relationship are [[Conditional independence|conditionally independent]] given the values of their parents.

This type of graphical model is known as a directed graphical model, [[Bayesian network]], or belief network. Classic [[machine learning]] methods like [[hidden Markov models]], [[neural networks]] and newer models such as [[Variablevariable order Markov modelsmodel]]s can be considered as special cases of Bayesian networks.

Graphical models with undirected edges are generally called [[Markov random fieldsfield]]s or [[Markov networksnetwork]]s.

Applications of graphical models include modelling of [[gene regulatory network]]s, [[speech recognition]], gene finding, [[computer vision]] and diagnosis of diseases.