Kullback-Leibler Divergence for the Normal-Gamma Distribution
classification
🧮 math.ST
q-bio.NCstat.TH
keywords
divergencedistributionkullback-leiblernormal-gammaapplicationsbayesiancomplexityconjugate
read the original 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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.