A methodology is developed to assign, from an observed sample, a joint-probability
distribution to a set of continuous variables. The algorithm proposed performs this assignment by
mapping the original variables onto a jointly-Gaussian set. The map is built iteratively, ascending
the log-likelihood of the observations, through a series of steps that move the marginal distributions
along a random set of orthogonal directions towards normality.
Publié le : 2010-03-15
Classification:
Density estimation,
machine learning,
maximum likelihood,
34A50,
65C30,
65L20,
60H35
@article{1266935020,
author = {Tabak, Esteban G. and Vanden-Eijnden, Eric},
title = {Density estimation by dual ascent of the log-likelihood},
journal = {Commun. Math. Sci.},
volume = {8},
number = {1},
year = {2010},
pages = { 217-233},
language = {en},
url = {http://dml.mathdoc.fr/item/1266935020}
}
Tabak, Esteban G.; Vanden-Eijnden, Eric. Density estimation by dual ascent of the log-likelihood. Commun. Math. Sci., Tome 8 (2010) no. 1, pp. 217-233. http://gdmltest.u-ga.fr/item/1266935020/