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2015 Volume 24 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/3524

Title: Dynamic Parameter Estimation Based on Minimum Cross-Entropy Method for Combining Information Sources
Authors: Sečkárová, Vladimíra
Keywords: minimum cross-entropy principle
Kullback-Leibler divergence
dynamic diffusion estimation
Issue Date: 2015
Publisher: Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences
Citation: Pliska Studia Mathematica Bulgarica, Vol. 24, No 1, (2015), 181p-188p
Abstract: When combining information sources, e.g. measuring devices or experts, we deal with two problems: which combining method to choose (linear combination, geometric mean) and how to measure the reliability of the sources, i.e. how to assign the weights to them. Inspired by [5] we introduce a method which overcomes such shortcomings. Proposed method, based on minimization of the Kullback-Leibler divergence with specific constraints, directly combines data, i.e. probability vectors, thus no additional step to obtain the weights is needed. The detailed description of the proposed method and a comparison with recently introduced dynamic diffusion estimation [2], which heavily depends on the determination of the weights, form the core of this contribution. 2010 Mathematics Subject Classification: 94A17, 62L12.
URI: http://hdl.handle.net/10525/3524
ISSN: 0204-9805
Appears in Collections:2015 Volume 24

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