Bayesian network construction and simplified inference method based on causal chains

Yohei Ueda, Daisuke Ide, Masaomi Kimura

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A Bayesian network (BN) is a probabilistic graphical model that represents random variables of causal relationships as a directed acyclic graph. There are many methods to construct BNs. These methods decide a BN structure whose likelihood is best in candidates. However, the edges expressing causal relationships tend not to match the one manually obtained by a human, because it reflects the causality between events that do not occur. We should focus on causal relationship of events that occurs in the most of cases. Therefore, it is convenient to generate a BN based on causal chains. To generate a BN from causal chains, we propose an approach to get events and causal chains from diagnostics reports and infer events by using BN. Since causal chains in the report are definitive, probabilities in BNs can be limited to zero or one. Thus, we also propose a simplified algorithm for BN inference.

本文言語English
ホスト出版物のタイトルIntelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
ホスト出版物のサブタイトルIntegrating People and Intelligent Systems
出版社Springer Verlag
ページ438-443
ページ数6
ISBN(印刷版)9783319738871
DOI
出版ステータスPublished - 2018 1 1
イベント1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 - Dubai, United Arab Emirates
継続期間: 2018 1 72018 1 9

出版物シリーズ

名前Advances in Intelligent Systems and Computing
722
ISSN(印刷版)2194-5357

Other

Other1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018
CountryUnited Arab Emirates
CityDubai
Period18/1/718/1/9

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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