Neural Comp. NEW Faster Access
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Miller, G.
Right arrow Articles by Horn, D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Miller, G.
Right arrow Articles by Horn, D.

Neural Computation, Vol 10, 1925-1938, Copyright © 1998 by The MIT Press

LETTERS

Probability Density Estimation Using Entropy Maximization

Gad Miller and David Horn

We propose a method for estimating probability density functions and conditional density functions by training on data produced by such distributions. The algorithm employs new stochastic variables that amount to coding of the input, using a principle of entropy maximization. It is shown to be closely related to the maximum likelihood approach. The encoding step of the algorithm provides an estimate of the probability distribution. The decoding step serves as a generative mode, producing an ensemble of data with the desired distribution. The algorithm is readily implemented by neural networks, using stochastic gradient ascent to achieve entropy maximization.


This article has been cited by other articles:


Home page
 Genome ResHome page
N. V. Grishin, Y. I. Wolf, and E. V. Koonin
From Complete Genomes to Measures of Substitution Rate Variability Within and Between Proteins
Genome Res., July 1, 2000; 10(7): 991 - 1000.
[Abstract] [Full Text]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1998 by The MIT Press.