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→Types of graphical models: move Markov random field close to conditional random field, because they are closely related |
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This type of graphical model is known as a directed graphical model, [[Bayesian network]], or belief network. Classic machine learning models like [[hidden Markov models]], [[neural networks]] and newer models such as [[variable-order Markov model]]s can be considered special cases of Bayesian networks.
A Markov random field, also known as a Markov network, is a model over an [[undirected graph]]. A graphical model with many repeated subunits can be represented with [[plate notation]].▼
===Other types===
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|mr=1926166 | zbl = 1033.60008
|citeseerx=10.1.1.33.4906}}</ref>
▲* A [[Markov random field]], also known as a Markov network, is a model over an [[undirected graph]]. A graphical model with many repeated subunits can be represented with [[plate notation]].
* A [[conditional random field]] is a [[discriminative model]] specified over an undirected graph.
* A [[restricted Boltzmann machine]] is a [[Bipartite graph|bipartite]] [[generative model]] specified over an undirected graph.
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