### Resumen

© Springer-Verlag Berlin Heidelberg 2002. This paper presents new methods for generation of random Bayesian networks. Suchme thods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the “structure” of the network) and then, for the generated graph, conditional probability distributions. No algorithm in the literature currently offers guarantees concerning the distribution of generated Bayesian networks. Using tools from the theory of Markov chains, we propose algorithms that can generate uniformly distributed samples of directed acyclic graphs. We introduce methods for the uniform generation of multi-connected and singly-connected networks for a given number of nodes; constraints on node degree and number of arcs can be easily imposed. After a directed acyclic graphi s uniformly generated, the conditional distributions are produced by sampling Dirichlet distributions.

Idioma original | Inglés estadounidense |
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Título de la publicación alojada | Random generation of Bayesian networks |

Páginas | 366-376 |

Número de páginas | 11 |

ISBN (versión digital) | 3540001247, 9783540001249 |

Estado | Publicada - 1 ene 2002 |

Publicado de forma externa | Sí |

Evento | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duración: 1 ene 2018 → … |

### Serie de la publicación

Nombre | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volumen | 2507 |

ISSN (versión impresa) | 0302-9743 |

### Conferencia

Conferencia | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Período | 1/01/18 → … |

## Huella Profundice en los temas de investigación de 'Random generation of Bayesian networks'. En conjunto forman una huella única.

## Citar esto

Ide, J. S., & Cozman, F. G. (2002). Random generation of Bayesian networks. En

*Random generation of Bayesian networks*(pp. 366-376). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2507).