Graphical model: Difference between revisions

Revision as of 13:23, 30 April 2006

Revision as of 13:23, 30 April 2006

Line 15:

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 [[Machinemachine learning|Machinemachine learning]] methods like [[hidden Markov models|hidden Markov models]] or [[neural networks|neural networks]] can be considered as special cases of Bayesian networks.

There are also undirected graphical models, generally called [[Markov random fields|Markov random fields]] or [[Markov networks|Markov networks]]..