Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Probability Distributions using "Dirac" Kernels (via
  Rademacher-Walsh Polynomial Basis Functions)   In many applications (in particular information systems, such as pattern
recognition, machine learning, cheminformatics, bioinformatics to name but a
few) the assessment of uncertainty is essential - i.e., the estimation of the
underlying probability distribution function. More often than not, the form of
this function is unknown and it becomes necessary to non-parametrically
construct/estimate it from a given sample. One of the methods of choice to
non-parametrically estimate the unknown probability distribution function for a
given random variable (defined on binary space) has been the expansion of the
estimation function in Rademacher-Walsh Polynomial basis functions. In this
paper we demonstrate that the expansion of the probability distribution
function estimation in Rademacher-Walsh Polynomial basis functions is
equivalent to the expansion of the function estimation in a set of "Dirac
kernel" functions. The latter approach can ameliorate the computational
bottleneck and notational awkwardness often associated with the
Rademacher-Walsh Polynomial basis functions approach, in particular when the
binary input space is large.

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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: math
37
The categories of this paper are: supr-con
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The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th