Graphical model: Difference between revisions

Revision as of 13:23, 30 April 2006

Revision as of 13:25, 30 April 2006

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

ThereGraphical aremodels alsowith undirected graphicaledges models,are generally called [[Markov random fields|Markov random fields]] or [[Markov networks|Markov networks]]..

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