Graphical model: Difference between revisions

Revision as of 22:54, 11 July 2005

Revision as of 17:16, 15 July 2005

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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.

There are also undirected graphical models, also called Markov networks, in which graph separation encodes conditional independencies (these are also known as graphical Gaussian models, or GGMs).