TY - GEN
T1 - Probabilistic modeling of alarm observation delay in network diagnosis
AU - Hashimoto, Kazuo
AU - Matsumoto, Kazunori
AU - Shiratori, Norio
PY - 2000/12/1
Y1 - 2000/12/1
N2 - This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. Firstly, a fault model is denned by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose AIC (Akaike information criterion) is minimal. It is proved that by simulation that the AIC based hypothesis selection achieves the high precision in diagnosis.
AB - This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. Firstly, a fault model is denned by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose AIC (Akaike information criterion) is minimal. It is proved that by simulation that the AIC based hypothesis selection achieves the high precision in diagnosis.
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M3 - Conference contribution
AN - SCOPUS:84867810439
SN - 3540679251
SN - 9783540679257
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 734
EP - 744
BT - PRICAI 2000, Topics in Artificial Intelligence - 6th Pacific Rim International Conference on Artificial Intelligence, Proceedings
T2 - 6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000
Y2 - 28 August 2000 through 1 September 2000
ER -