Name
Abstract | ||
|---|---|---|
Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster's combination rule to obtain the fusion result. Finally, with the aid of pignistic probability transformation, the combination result is converted to posterior probability distribution. A numerical example and a real application in target recognition show the efficiency of the proposed method. The proposed method can be seen as the generalized Bayesian update. If the information quality is not considered, the proposed method degenerates to the classical Bayesian update. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/e21010005 | ENTROPY |
Keywords | Field | DocType |
Bayesian update,information quality,Dempster-Shafer evidence theory,basic probability assignment,target recognition,prior probability distribution,posterior probability distribution | Pignistic probability,Data mining,Mathematical optimization,Discounting,Posterior probability,Sensor fusion,Prior probability,Mathematics,Bayesian probability,Information quality | Journal |
Volume | Issue | ISSN |
21 | 1 | 1099-4300 |
Citations | PageRank | References |
1 | 0.35 | 53 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuting Li | 1 | 1 | 1.02 |
| Fuyuan Xiao | 2 | 201 | 19.11 |