Accuracy of Sequential Bayesian Information Fusion

J.R.J. Nunnink and G. Pavlin (The Netherlands)


Probabilistic Reasoning, Intelligent Information Systems, Bayesian Networks, Fusion Accuracy, Decision Making.


Sequential Bayesian information fusion is a process in which streams of observations from multiple sources are fused in order to form a more complete and accurate sit uation assessment. Allthough the information sources are potentially unreliable, and we generally have little control over them, it is possible to directly influence the accuracy of the fusion through the parameters of the Bayesian net work (BN). In this paper we analyse the expected accuracy of decision making based on fusion with semi-dynamic BNs, in terms of the model parameters and decision thresh olds.

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