Communications in Mathematical Sciences

Density estimation by dual ascent of the log-likelihood

Esteban G. Tabak and Eric Vanden-Eijnden

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Abstract

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.

Article information

Source
Commun. Math. Sci., Volume 8, Number 1 (2010), 217-233.

Dates
First available in Project Euclid: 23 February 2010

Permanent link to this document
https://projecteuclid.org/euclid.cms/1266935020

Mathematical Reviews number (MathSciNet)
MR2655907

Zentralblatt MATH identifier
1189.62063

Subjects
Primary: 34A50 65C30: Stochastic differential and integral equations 65L20: Stability and convergence of numerical methods 60H35: Computational methods for stochastic equations [See also 65C30]

Keywords
Density estimation machine learning maximum likelihood

Citation

Tabak, Esteban G.; Vanden-Eijnden, Eric. Density estimation by dual ascent of the log-likelihood. Commun. Math. Sci. 8 (2010), no. 1, 217--233. https://projecteuclid.org/euclid.cms/1266935020


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