Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Some Statistic and Information-theoretic Results on Arithmetic Average
  Density Fusion   Finite mixture such as the Gaussian mixture is a flexible and powerful
probabilistic modeling tool for representing the multimodal distribution widely
involved in many estimation and learning problems. The core of it is
representing the target distribution by the arithmetic average (AA) of a finite
number of sub-distributions which constitute the mixture. The AA fusion has
demonstrated compelling performance for both single-sensor and multi-sensor
estimator design. In this paper, some statistic and information-theoretic
results are given on the AA fusion approach, including its covariance
consistency, mean square error, mode-preservation capacity, mixture information
divergence and principles for fusing/mixing weight design. In particular, based
on the concept of conservative fusion, the relationship of the AA fusion with
the existing conservative fusion approaches such as covariance union and
covariance intersection is exposed. The best fit of the mixture is formulated
as a max-min problem, proving the sub-optimality of the AA fusion. Linear
Gaussian models are considered for illustration and simulation comparison.

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The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an