Fetching the paper…

Reading the bibliography…

Kullback-Leibler Divergence for the Normal-Gamma Distribution · Around

2016

Kullback-Leibler Divergence for the Normal-Gamma Distribution

Soch, Joram, Allefeld, Carsten

Original
PDF
Post on X

Understand

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.

Built on

Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RSJ (1995): “Statistical parametric maps in functional imaging: A general linear approach”
1995
Earlier work this paper cites.
Penny WD (2001): “KL-Divergences of Normal, Gamma, Dirichlet and Wishart densities”
Original
2001
Earlier work this paper cites.

Similar

Bishop CM (2006): Pattern Recognition and Machine Learning
2006
Cited alongside, same era.
Koch KR (2007): Introduction to Bayesian Statistics
2007
Cited alongside, same era.

Then

Bogler C, Bode S, Haynes JD (2013): “Orientation pop-out processing in human visual cortex”
2013
Later among the works it cites.
Duchi J (2014): “Derivations for Linear Algebra and Optimization”
2014
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…