Name
Abstract | ||
|---|---|---|
Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relation. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and event prediction with good results. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence | temporal range,temporal node,temporal bayesian network,fault diagnosis,temporal interval,industrial diagnosis,temporal nodes bayesian network,event prediction,state change,dynamic bayesian network,temporal reasoning,bayesian network |
DocType | Volume | ISBN |
Journal | abs/1301.6675 | 1-55860-614-9 |
Citations | PageRank | References |
26 | 1.85 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Arroyo-Figueroa | 1 | 170 | 22.16 |
| Luis Enrique Suear | 2 | 26 | 1.85 |