Random generation of Bayesian networks

Jaime S. Ide, Fabio G. Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Scopus citations

Abstract

© 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.
Original languageAmerican English
Title of host publicationRandom generation of Bayesian networks
Pages366-376
Number of pages11
ISBN (Electronic)3540001247, 9783540001249
StatePublished - 1 Jan 2002
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2507
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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