A new framework for histogram-based mutual information estimation of probability distributions equipped with density functions in ( d , B( d )) is presented in this work. A general histogram-based estimate is proposed, considering nonproduct data-dependent partitions, and sufficient conditions are stipulated to guarantee a strongly consistent estimate for mutual information. Two emblematic families of density-free strongly consistent estimates are derived from this result, one based on

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... ally equivalent blocks (the Gessaman's partition) and the other, on a tree-structured vector quantization scheme.