A tight lower bound on the mutual information of a binary and an arbitrary finite random variable as a function of the variational distance
Arno G. Stefani, Johannes B. Huber, Christophe Jardin, Heinrich Sticht · 2014
In this paper a numerical method is presented, which finds a tight lower bound for the mutual information between a binary and an arbitrary finite random variable with joint distributions that have variational distance to a known joint distribution not greater than a known value. This lower bound can be applied to mutual information estimation with confidence intervals.