Kullback-Leibler Divergence for the Normal-Gamma Distribution
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Nov 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021Scale Mixtures of Neural Network Gaussian Processescited by 5
- 2021Active Learning of Continuous-time Bayesian Networks through Interventionscited by 2
- 2026Mixtures of Transparent Local Modelscited by 0
- 2026Mixtures of Transparent Local Models15 Jan 2026
- 2021Scale Mixtures of Neural Network Gaussian Processes3 Jul 2021
- 2021Active Learning of Continuous-time Bayesian Networks through Interventions31 May 2021