Bayesian network construction and simplified inference method based on causal chains

Yohei Ueda, Daisuke Ide, Masaomi Kimura

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

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIntelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
Subtitle of host publicationIntegrating People and Intelligent Systems
PublisherSpringer Verlag
Pages438-443
Number of pages6
ISBN (Print)9783319738871
DOIs
Publication statusPublished - 2018 Jan 1
Event1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 - Dubai, United Arab Emirates
Duration: 2018 Jan 72018 Jan 9

Publication series

NameAdvances in Intelligent Systems and Computing
Volume722
ISSN (Print)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

Keywords

  • Bayesian network
  • Case frame
  • Deep cases

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Ueda, Y., Ide, D., & Kimura, M. (2018). Bayesian network construction and simplified inference method based on causal chains. In Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems (pp. 438-443). (Advances in Intelligent Systems and Computing; Vol. 722). Springer Verlag. https://doi.org/10.1007/978-3-319-73888-8_68