Estimating Probability Distributions using "Dirac" Kernels (via Rademacher-Walsh Polynomial Basis Functions)
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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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