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==Types of graphical models==
Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or [[Factor graph|factorized]] representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, [[Bayesian network]]s and [[Markov random field]]s. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.<ref name=koller09>{{cite book
|author=
|author2=
|title=Probabilistic Graphical Models
|url=http://pgm.stanford.edu/
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|doi=
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|author2-link=Nir Friedman
}}</ref>▼
|author-link=Daphne Koller
▲ }}</ref>
===Bayesian network===
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* {{cite book
|author=Cowell, Robert G.
|author2=
|title=Probabilistic networks and expert systems |publisher=Springer |___location=Berlin |year=1999 |pages= |isbn=978-0-387-98767-5 |doi= |accessdate= |mr=1697175 |ref=cowell |author2-link=Philip Dawid }} A more advanced and statistically oriented book
* {{cite book |author=Jensen, Finn |title=An introduction to Bayesian networks |publisher=Springer |___location=Berlin |year=1996 |pages= |isbn=978-0-387-91502-9 |doi= |accessdate=}}
* {{Cite book
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*{{Cite journal | last1 = Jordan | first1 = M. I. | authorlink=Michael I. Jordan| doi = 10.1214/088342304000000026 | title = Graphical Models | journal = Statistical Science | volume = 19 | pages = 140–155| year = 2004 | pmid = | pmc = }}
*{{Cite journal|last=Ghahramani|first=Zoubin|date=May 2015|title=Probabilistic machine learning and artificial intelligence
===Other===
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