Name
Title | ||
|---|---|---|
Probabilistic reasoning and multiple-expert methodology for correlated objective data |
Abstract | ||
|---|---|---|
In this paper, a numerical expert system using probabilistic reasoning with influence structure generated from the observed data is demonstrated. Instead of using an expert to encode the influence diagram, the system has the capability to construct it from the objective data. In cases where data are correlated, instead of compromising the performance by wrestling with different influence structures based on the assumption that all the environment variables are observed, we incorporated the flexibility of including unobservable variables in our system. The resulting methodology minimised the intervention of a domain expert during modelling and improved the system performance. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1016/S0954-1810(96)00035-0 | Artificial Intelligence in Engineering |
Keywords | DocType | Volume |
probabilistic network,bayesian inference,multiple-expert system,unobservable variables | Journal | 12 |
Issue | ISSN | Citations |
1 | 0954-1810 | 1 |
PageRank | References | Authors |
0.35 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C K Kwoh | 1 | 559 | 46.55 |
| Duncan Fyfe Gillies | 2 | 97 | 17.86 |