TY - GEN
T1 - Bayesian model selection for diagnostics
AU - Provan, Gregory
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Model-Based Diagnosis (MBD) addresses the task of isolating the most likely fault given a set of system measurements. The model used for diagnostics is critical to this isolation task, yet little work exists for specifying which type of model is best suited to MBD. We apply Bayesian model selection to identify the model that optimizes a diagnostics task, according to key fault-isolation metrics. We illustrate our approach using a tank benchmark system, demonstrating the trade-offs possible by using different models for this benchmark.
AB - Model-Based Diagnosis (MBD) addresses the task of isolating the most likely fault given a set of system measurements. The model used for diagnostics is critical to this isolation task, yet little work exists for specifying which type of model is best suited to MBD. We apply Bayesian model selection to identify the model that optimizes a diagnostics task, according to key fault-isolation metrics. We illustrate our approach using a tank benchmark system, demonstrating the trade-offs possible by using different models for this benchmark.
KW - Bayesian model selection
KW - Diagnostics
UR - https://www.scopus.com/pages/publications/84951783346
U2 - 10.1007/978-3-319-23781-7_20
DO - 10.1007/978-3-319-23781-7_20
M3 - Conference proceeding
AN - SCOPUS:84951783346
SN - 9783319237800
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 256
BT - Model and Data Engineering - 5th International Conference, MEDI 2015, Proceedings
A2 - Manolopoulos, Yannis
A2 - Bellatreche, Ladjel
PB - Springer Verlag
T2 - 5th International Conference on Model and Data Engineering, MEDI 2015
Y2 - 26 September 2015 through 28 September 2015
ER -