# Bayesian network

## Statistical model / From Wikipedia, the free encyclopedia

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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 dependencies via a directed acyclic graph (DAG). It is one of several forms of causal notation. 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.

Part of a series on |

Bayesian statistics |
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Posterior = Likelihood × Prior ÷ Evidence |

Background |

Model building |

Posterior approximation |

Estimators |

Evidence approximation |

Model evaluation |

Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (*e.g.* speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.