Title: A Measure of Dependence Between Discrete and Continuous Variables,
Abstract: Mutual Information (MI) is an useful tool for the recognition of mutual
dependence berween data sets. Differen methods for the estimation of MI have
been developed when both data sets are discrete or when both data sets are
continuous. The MI estimation between a discrete data set and a continuous data
set has not received so much attention. We present here a method for the
estimation of MI for this last case based on the kernel density approximation.
The calculation may be of interest in diverse contexts. Since MI is closely
related to Jensen Shannon divergence, the method here developed is of
particular interest in the problem of sequence segmentation.