Confidence intervals for the mutual information
by Arno G. Stefani; Johannes B. Huber; Christophe Jardin; Heinrich Sticht
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 1, No. 3, 2014

Abstract: By combining a bound on the absolute value of the difference of mutual information between two joint probability distributions with a fixed variational distance, and a bound on the probability of a maximal deviation in variational distance between a true joint probability distribution and an empirical joint probability distribution, confidence intervals for the mutual information of two random variables with finite alphabets are established. Different from previous results, these confidence intervals do not need any assumptions on the distribution or the sample size.

Online publication date: Wed, 31-Dec-2014

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