Mutual Information – Example with categorical variables
Mutual information and its cousin, the Uncertainty coefficient (Theil’s U) are useful tools from Information Theory for discovering dependencies between variables that are not necessary described by a linear relationship.
Plenty of good material already exists on the subject: see Section 1.6 in “Pattern Recognition and Machine Learning” by Bishop, freely available as PDF online. What I hope to contribute here are some trivial examples, to help build intuition, based on categorical/multinomial variables.[Read More]
KL Divergence Online Demo
To try out Shiny, I created an interactive visualization for Kullback-Leibler divergence (or KL Divergence). Right now, it only supports two univariate Gaussians, which should be sufficient to build some intuition. [Read More]