International Journal of Computational Intelligence Systems

Volume 1, Issue 2, June 2008, Pages 188 - 201

A Comparative Study of Various Probability Density Estimation Methods for Data Analysis

Authors
Alex Assenza, Maurizio Valle, Michel Verleysen
Corresponding Author
Alex Assenza
Available Online 1 June 2008.
DOI
https://doi.org/10.2991/ijcis.2008.1.2.9How to use a DOI?
Keywords
Probability Density Function estimation, Parzen windows, finite Gaussian mixtures
Abstract
Probability density estimation (PDF) is a task of primary importance in many contexts, including Bayesian learning and novelty detection. Despite the wide variety of methods at disposal to estimate PDF, only a few of them are widely used in practice by data analysts. Among the most used methods are the histograms, Parzen windows, vector quantization based Parzen, and finite Gaussian mixtures. This paper compares these estimations methods from a practical point of view, i.e. when the user is faced to various requirements from the applications. In particular it addresses the question of which method to use when the learning sample is large or small, and of the computational complexity resulting from the choice (by cross-validation methods) of external parameters such as the number of kernels and their widths in kernel mixture models, the robustness to initial conditions, etc. Expected behaviour of the estimation algorithms is drawn from an algorithmic perspective; numerical experiments are used to illustrate these results.
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
1 - 2
Pages
188 - 201
Publication Date
2008/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2008.1.2.9How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Alex Assenza
AU  - Maurizio Valle
AU  - Michel Verleysen
PY  - 2008
DA  - 2008/06
TI  - A Comparative Study of Various Probability Density Estimation Methods for Data Analysis
JO  - International Journal of Computational Intelligence Systems
SP  - 188
EP  - 201
VL  - 1
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2008.1.2.9
DO  - https://doi.org/10.2991/ijcis.2008.1.2.9
ID  - Assenza2008
ER  -