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.
| 元の言語 | 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 |
| 出版者 | Springer Verlag |
| ページ | 438-443 |
| ページ数 | 6 |
| ISBN（印刷物） | 9783319738871 |
| DOI | |
| 出版物ステータス | 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 7 → 2018 1 9 |
出版物シリーズ
| 名前 | Advances in Intelligent Systems and Computing |
|---|---|
| 巻 | 722 |
| ISSN（印刷物） | 2194-5357 |
Other
| Other | 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 |
|---|---|
| 国 | United Arab Emirates |
| 市 | Dubai |
| 期間 | 18/1/7 → 18/1/9 |
Fingerprint
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science(all)
これを引用
Bayesian network construction and simplified inference method based on causal chains. / Ueda, Yohei; Ide, Daisuke; Kimura, Masaomi.
Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Springer Verlag, 2018. p. 438-443 (Advances in Intelligent Systems and Computing; 巻 722).研究成果: Conference contribution
}
TY - GEN
T1 - Bayesian network construction and simplified inference method based on causal chains
AU - Ueda, Yohei
AU - Ide, Daisuke
AU - Kimura, Masaomi
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Bayesian network
KW - Case frame
KW - Deep cases
UR - http://www.scopus.com/inward/record.url?scp=85040219930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040219930&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73888-8_68
DO - 10.1007/978-3-319-73888-8_68
M3 - Conference contribution
AN - SCOPUS:85040219930
SN - 9783319738871
T3 - Advances in Intelligent Systems and Computing
SP - 438
EP - 443
BT - Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
PB - Springer Verlag
ER -