Green's function based unparameterised multi-dimensional kernel density and likelihood ratio estimator
Machine Learning
2012-08-22 v2 Statistics Theory
Statistics Theory
Abstract
This paper introduces a probability density estimator based on Green's function identities. A density model is constructed under the sole assumption that the probability density is differentiable. The method is implemented as a binary likelihood estimator for classification purposes, so issues such as mis-modeling and overtraining are also discussed. The identity behind the density estimator can be interpreted as a real-valued, non-scalar kernel method which is able to reconstruct differentiable density functions.
Cite
@article{arxiv.1112.2093,
title = {Green's function based unparameterised multi-dimensional kernel density and likelihood ratio estimator},
author = {Peter Kovesarki and Ian C. Brock and A. Elizabeth Nuncio Quiroz},
journal= {arXiv preprint arXiv:1112.2093},
year = {2012}
}
Comments
7 pages, 4 figures. JPCS accepted it as a proceedings to the ACAT workshop