Literature DB >> 16106588

Edgeworth approximation of multivariate differential entropy.

Marc M Van Hulle1.   

Abstract

We develop the general, multivariate case of the Edgeworth approximation of differential entropy and show that it can be more accurate than the nearest-neighbor method in the multivariate case and that it scales better with sample size. Furthermore, we introduce mutual information estimation as an application.

Mesh：

Year:  2005        PMID： 16106588     DOI： 10.1162/0899766054323026

Source DB:  PubMed          Journal:  Neural Comput        ISSN： 0899-7667            Impact factor:   2.026


  5 in total

1.  Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy.

Authors:  Lianet Contreras Rodríguez; Evaristo José Madarro-Capó; Carlos Miguel Legón-Pérez; Omar Rojas; Guillermo Sosa-Gómez
Journal:  Entropy (Basel)       Date:  2021-04-30       Impact factor: 2.524

2.  Discovering Pair-wise Synergies in Microarray Data.

Authors:  Yuan Chen; Dan Cao; Jun Gao; Zheming Yuan
Journal:  Sci Rep       Date:  2016-07-29       Impact factor: 4.379

3.  Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO.

Authors:  Tue M Vu; Ashok K Mishra; Goutam Konapala
Journal:  Entropy (Basel)       Date:  2018-01-09       Impact factor: 2.524

4.  Mutual information estimation reveals global associations between stimuli and biological processes.

Authors:  Taiji Suzuki; Masashi Sugiyama; Takafumi Kanamori; Jun Sese
Journal:  BMC Bioinformatics       Date:  2009-01-30       Impact factor: 3.169

5.  LPI Radar Waveform Recognition Based on Time-Frequency Distribution.

Authors:  Ming Zhang; Lutao Liu; Ming Diao
Journal:  Sensors (Basel)       Date:  2016-10-12       Impact factor: 3.576
  5 in total

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