A Kernel-Based Calculation of Information on a Metric Space
Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual information between stimuli and spiking responses; the spac...
Main Authors: | Conor J. Houghton, R. Joshua Tobin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2013-10-01
|
Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/15/10/4540 |
Similar Items
-
INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES
by: Songhua Liu, et al.
Published: (2012-02-01) -
Kernel Density Estimation on the Siegel Space with an Application to Radar Processing
by: Emmanuel Chevallier, et al.
Published: (2016-11-01) -
Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO
by: Tue M. Vu, et al.
Published: (2018-01-01) -
Multivariate kernel density estimation with a parametric support
by: Jolanta Jarnicka
Published: (2009-01-01) -
Estimating Mutual Information for Spike Trains: A Bird Song Example
by: Jake Witter, et al.
Published: (2023-10-01)