A Bounded Error, Anytime, Parallel Algorithm for Exact Bayesian Network Structure Learning

Brandon Michael Malone, Changhe Yuan

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

Bayesian network structure learning is NP-hard. Several anytime structure learning algorithms have been proposed which guarantee to learn optimal networks if given enough resources. In this paper, we describe a general purpose, anytime search algorithm with bounded error that also guarantees optimality. We give an efficient, sparse representation of a key data structure for structure learning. Empirical results show our algorithm often finds better networks more quickly than state of the art methods. They also highlight accepting a small, bounded amount of suboptimality can reduce the memory and runtime requirements of structure learning by several orders of magnitude.
Originalspråkengelska
Titel på gästpublikationProceedings of the Sixth European Workshop on Probabilistic Graphical Models, PGM'12
RedaktörerAndrés Cano, Manuel Gómez-Olmedo, Thomas Nielsen
FörlagDECSAI, University of Granada
Utgivningsdatum2012
ISBN (elektroniskt)978-84-15536-57-4
StatusPublicerad - 2012
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangProbabilistic Graphical Models (PGM2012) Workshop - Granada, Spanien
Varaktighet: 19 sep 201221 sep 2012
Konferensnummer: 6

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  • 113 Data- och informationsvetenskap

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