Law recognition via histogram-based estimation

In this paper, we study the problem of recognizing an unknown probability density function from one of its sample which is of interest in signal and image processing or telecommunication applications. By opposition with the classical Kolmogorov-Smirnov method based on empirical cumulative functions, we consider histogram estimators of the density itself built from our data. Those histograms are generated via model selection, more specifically via a codelength-based information criterion. From the histograms, we may compute a Kullback-Leibler distance to any theoretical law which is used to complete the recognition. We apply this histogram-based method for law recognition in a theoretical setup where the true density is known as well as in a real setup where data come from radio channel propagation experimentation.