Name
Abstract | ||
|---|---|---|
Factor tree inference algorithm (FTI) is an exact inference algorithm for diagnostic Bayesian networks (DBNs). Through computation sharing, the efficiency of FTI can be superior to conventional exact inference algorithms when answering multiple queries. However, there are circumstances where the factor tree is cyclic; FTI can not perform in these situations. In this article, we propose a factor graph inference algorithm (FGI). FGI can perform when the factor graph is cyclic, and reduces to FTI when it is acyclic. We demonstrate the benefit of FGI on a real-world DBN. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICNC.2011.6022121 | ICNC |
Keywords | Field | DocType |
computation sharing,diagnostic bayesian networks,exact inference,bayesian method,algorithm design,factor graph,artificial intelligent,probabilistic logic,artificial intelligence,bayesian network,bayesian methods,algorithm design and analysis,operations research,graph theory | Factor graph,Variable elimination,Frequentist inference,Bayesian inference,Computer science,Inference,Algorithm,Influence diagram,Artificial intelligence,Adaptive neuro fuzzy inference system,Machine learning,Belief propagation | Conference |
Volume | Issue | Citations |
1 | null | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxiao Huang | 1 | 10 | 2.25 |
| Haiyang Jia | 2 | 28 | 5.49 |
| Yungang Zhu | 3 | 31 | 4.52 |
| Dayou Liu | 4 | 814 | 68.17 |