Name
Abstract | ||
|---|---|---|
This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty. In the context of incompleteness of reliability data and inconsistencies between the reliability model and the system modeled, the evidence theory is more suitable to manage this epistemic uncertainty. We propose to adapt the Bayesian Network model of reliability in order to integrate the evidence theory and then to produce an Evidential Network. Three examples are proposed to observe the propagation mechanism of the uncertainty through the network and its influence on the system reliability. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.4304/jcp.2.1.33-43 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
Reliability, Epistemic Uncertainty, Dempster Shafer Theory, Bayesian Networks, Evidential Networks | Complex system,Uncertainty quantification,Computer science,Bayesian network,Artificial intelligence,Dempster–Shafer theory,Machine learning,Reliability model | Journal |
Volume | Issue | ISSN |
2 | 1 | 1796-203X |
Citations | PageRank | References |
12 | 0.95 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christophe Simon | 1 | 162 | 14.35 |
| Philippe Weber | 2 | 44 | 3.49 |
| E. Levrat | 3 | 145 | 10.60 |