Entropy expressions for multivariate continuous distributions

Authors:
G. A. Darbellay;I. Vajda
Affiliations:
Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 11

Formulas for Rényi information and related measures for univariate distributions

Information Sciences: an International Journal
Expressions for Rényi and Shannon entropies for bivariate distributions

Information Sciences—Informatics and Computer Science: An International Journal
Measuring stochastic dependence using φ -divergence

Journal of Multivariate Analysis
Maximum entropy characterizations of the multivariate Liouville distributions

Journal of Multivariate Analysis
Multivariate dynamic information

Journal of Multivariate Analysis
Multivariate maximum entropy identification, transformation, and dependence

Journal of Multivariate Analysis
Universal Estimation of Information Measures for Analog Sources

Foundations and Trends in Communications and Information Theory
Efficient optimization of information-theoretic exploration in SLAM

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Logarithmic Sobolev inequalities for information measures

IEEE Transactions on Information Theory
Differential entropy of multivariate neural spike trains

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Objective priors from maximum entropy in data classification

Information Fusion

Quantified Score

Hi-index	754.90

Visualization

Abstract

Analytical formulas for the entropy and the mutual information of multivariate continuous probability distributions are presented