Kullback-Leibler Divergence for the Normal-Gamma Distribution
Statistics Theory
2016-11-07 v1 Neurons and Cognition
Statistics Theory
Abstract
We derive the Kullback-Leibler divergence for the normal-gamma distribution and show that it is identical to the Bayesian complexity penalty for the univariate general linear model with conjugate priors. Based on this finding, we provide two applications of the KL divergence, one in simulated and one in empirical data.
Cite
@article{arxiv.1611.01437,
title = {Kullback-Leibler Divergence for the Normal-Gamma Distribution},
author = {Joram Soch and Carsten Allefeld},
journal= {arXiv preprint arXiv:1611.01437},
year = {2016}
}
Comments
10 pages, 2 figures