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

T2 - 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018

Y2 - 7 January 2018 through 9 January 2018

ER -