A Parallel Sampling Method for Bayesian Networks

Yoshiki Kobari, Masaomi Kimura

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

This paper describes the method to estimate probabilities on Bayesian belief networks (BNs). A BN has nodes showing random variables and shows cause and effect relationships among nodes as a graph. We calculate posterior probabilities, and then estimate the uncertain plural events. As one of the method for estimating probabilities in BN is stochastic sampling. The method has known as taking time to calculate as BN become larger and more complex. Therefore, this paper proposes a parallel sampling method on BN. Then a BN need efficiently dividing to generate samples in parallel, therefore we use community detection. The number of nodes, which have mutual dependence among nodes, is the least to use community detection. In addition, pipeline processing as generating samples, which have mutual dependence reduces waiting time causing by existing them.

元の言語English
ホスト出版物のタイトルProceedings - 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017
出版者Institute of Electrical and Electronics Engineers Inc.
ページ113-117
ページ数5
2018-February
ISBN(電子版)9781538629413
DOI
出版物ステータスPublished - 2018 2 16
イベント1st International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017 - Budapest, Hungary
継続期間: 2017 10 202017 10 22

Other

Other1st International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017
Hungary
Budapest
期間17/10/2017/10/22

    フィンガープリント

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

これを引用

Kobari, Y., & Kimura, M. (2018). A Parallel Sampling Method for Bayesian Networks. : Proceedings - 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017 (巻 2018-February, pp. 113-117). [8294170] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCSIC.2017.11