Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Error Analysis for the Particle Filter: Methods and Theoretical Support   The particle filter is a popular Bayesian filtering algorithm for use in
cases where the state-space model is nonlinear and/or the random terms (initial
state or noises) are non-Gaussian distributed. We study the behavior of the
error in the particle filter algorithm as the number of particles gets large.
After a decomposition of the error into two terms, we show that the difference
between the estimator and the conditional mean is asymptotically normal when
the resampling is done at every step in the filtering process. Two
nonlinear/non-Gaussian examples are tested to verify this conclusion.

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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: eess
3
The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas