Fusion of Probability Density Functions

Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of conti...

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Bibliographic Details
Main Authors:	Djuric, P.M (Author), El-Laham, Y. (Author), Hlawatsch, F. (Author), Koliander, G. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	Bayesian fusion Bayesian networks Chernoff fusion covariance intersection Covariance intersection Data handling forecasting Gaussian distribution Holder mean Hölder mean information fusion Information fusion Kullback Leibler divergence Kullback-Leibler divergence linear Gaussian model Linear Gaussian model Machine learning model averaging Model averaging multisensor signal processing pooling function Pooling function probabilistic machine learning Probabilistic machine learning probabilistic opinion pooling Probabilistic opinion pooling Probabilistics Probability density function Random variables sensor network Sensor networks Sensors network Signal processing supra-Bayesian fusion Supra-bayesian fusion Target tracking target tracking. Target tracking. Targets tracking α-divergence Α-divergence
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Fusion of Probability Density Functions

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