Investigation of Alternative Measures for Mutual Information

Mutual information I(X;Y) is a useful definition in theory to estimate how much the random variable Y holds about X. One way define mutual by comparing joint distribution of X and with product marginals through Kullback-Leibler (KL) divergence. If two distributions are close each other there will be almost no leakage from since variables being independent. In discrete setting has nice interpretation many bits reveals However, continuous case we do not have same reasoning. This fact enables us try different metrics or divergences information. this paper, evaluating form alternatives case. We deploy methods bound these evaluate their performances.