Editing Bayesian network (section)

{{Short description|Statistical model}}
{{more footnotes needed|date=February 2011}}
{{Bayesian statistics}}
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A '''Bayesian network''' (also known as a '''Bayes network''', '''Bayes net''', '''belief network''', or '''decision network''') is a [[probabilistic graphical model]] that represents a set of variables and their [[Conditional dependence|conditional dependencies]] via a [[directed acyclic graph]] (DAG).<ref>{{Cite book |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9780470061572 |title=Encyclopedia of Statistics in Quality and Reliability |date=2007-12-14 |publisher=Wiley |isbn=978-0-470-01861-3 |editor-last=Ruggeri |editor-first=Fabrizio |edition=1 |pages=1 |language=en |doi=10.1002/9780470061572.eqr089 |editor-last2=Kenett |editor-first2=Ron S. |editor-last3=Faltin |editor-first3=Frederick W.}}</ref> While it is one of several forms of [[causal notation]], causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor.  For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

Efficient algorithms can perform [[inference]] and [[machine learning|learning]] in Bayesian networks. Bayesian networks that model sequences of variables (''e.g.'' [[speech recognition|speech signals]] or [[peptide sequence|protein sequences]]) are called [[dynamic Bayesian network]]s. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called [[influence diagram]]s.

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