# Kullback-Leibler Divergence for the Normal-Gamma Distribution

arXiv: https://arxiv.org/abs/1611.01437 · HTML: https://mlsanity.com/paper/1611.01437/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Scale Mixtures of Neural Network Gaussian Processes](https://mlsanity.com/paper/2107.01408/) (2021)
- [Active Learning of Continuous-time Bayesian Networks through Interventions](https://mlsanity.com/paper/2105.14742/) (2021)
- [Mixtures of Transparent Local Models](https://mlsanity.com/paper/2601.10541/) (2026)

## Abstract (arXiv)

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.