Estimation of Mutual Information based on a reverse Jensen inequality approach
The RJE (Reverse-Jensen MI Estimator) is based on the recent paper Fritschek et al., "Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison", accepted at SPAWC 2020
The code is a shortened version of the colab code by Ben Poole, which shows differences in mutual information estimators, based on their analyses in the paper Poole et al., "On Variational Bounds of Mutual Information", ICML 2019
We removed the interpolation bounds, and unnecessary code due to those bounds, as we want to focus on simpler methods, and 1) added the recent SMILE estimator, based on the paper Song et al., "Understanding the Limitations of Variational Mutual Information Estimators", ICLR 2020, 2) added our RJE Estimator, and 3) added wireless channel models and supporting functions.