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Entropy 2019, 21(1), 5; https://doi.org/10.3390/e21010005

Bayesian Update with Information Quality under the Framework of Evidence Theory

School of Computer and Information Science, Southwest University, Chongqing 400715, China
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Received: 4 November 2018 / Revised: 28 November 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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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. View Full-Text
Keywords: Bayesian update; information quality; Dempster-Shafer evidence theory; basic probability assignment; target recognition; prior probability distribution; posterior probability distribution Bayesian update; information quality; Dempster-Shafer evidence theory; basic probability assignment; target recognition; prior probability distribution; posterior probability distribution
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Li, Y.; Xiao, F. Bayesian Update with Information Quality under the Framework of Evidence Theory. Entropy 2019, 21, 5.

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