Name
Abstract | ||
|---|---|---|
The noisy-or and its generalization noisy-max have been utilized to reduce the complexity of knowledge acquisition. In this paper, we present a new representation of noisy-max that allows for efficient inference in general Bayesian networks. Empirical studies show that our method is capable of computing queries in well-known large medical networks, QMR-DT and CPCS, for which no previous exact inference method has been shown to perform well. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence | efficient inference,new representation,generalization noisy-max,knowledge acquisition,multiplicative factorization,well-known large medical network,general bayesian network,empirical study,previous exact inference method |
DocType | Volume | ISBN |
Journal | abs/1301.6742 | 1-55860-614-9 |
Citations | PageRank | References |
18 | 1.11 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masami Takikawa | 1 | 23 | 4.25 |
| Bruce D'Ambrosio | 2 | 20 | 1.50 |