Originalsprog | Engelsk |
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Titel | Wiley StatsRef : Statistics Reference Online |

Forlag | Wiley |

Publikationsdato | 2016 |

Sider | 1-9 |

ISBN (Elektronisk) | 9781118445112 |

DOI | |

Status | Udgivet - 2016 |

### Resumé

The wide-spread use of Bayesian networks is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network formalism as well as some of the algorithmic developments in the area.

Navn | Wiley StatsRef: Statistics Reference Online |
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### Citer dette

*Wiley StatsRef: Statistics Reference Online*(s. 1-9). Wiley. Wiley StatsRef: Statistics Reference Online https://doi.org/10.1002/9781118445112.stat07360.pub2

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*Wiley StatsRef: Statistics Reference Online.*Wiley, Wiley StatsRef: Statistics Reference Online, s. 1-9. https://doi.org/10.1002/9781118445112.stat07360.pub2

**Bayesian Graphical Models.** / Jensen, Finn Verner; Nielsen, Thomas Dyhre.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Encyclopædiartikel › Forskning › peer review

TY - ENCYC

T1 - Bayesian Graphical Models

AU - Jensen, Finn Verner

AU - Nielsen, Thomas Dyhre

PY - 2016

Y1 - 2016

N2 - Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network formalism as well as some of the algorithmic developments in the area.

AB - Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network formalism as well as some of the algorithmic developments in the area.

U2 - 10.1002/9781118445112.stat07360.pub2

DO - 10.1002/9781118445112.stat07360.pub2

M3 - Encyclopedia chapter

SP - 1

EP - 9

BT - Wiley StatsRef

PB - Wiley

ER -