Exact Expressions for Kullback-Leibler Divergence for Univariate Distributions.

The Kullback-Leibler divergence (KL divergence) is a statistical measure that quantifies the difference between two probability distributions. Specifically, it assesses the amount of information that is lost when one distribution is used to approximate another. This concept is crucial in various fields, including information theory, statistics, and machine learning, as it helps in understanding how well a model represents the underlying data.

Exact Expressions for Kullback-Leibler Divergence for Multivariate and Matrix-Variate Distributions.

The Kullback-Leibler divergence is a measure of the divergence between two probability distributions, often used in statistics and information theory. However, exact expressions for it are not known for multivariate or matrix-variate distributions apart from a few cases. In this paper, exact expressions for the Kullback-Leibler divergence are derived for over twenty multivariate and matrix-variate distributions.