In this work, in many cases we compute the Jensen-Shannon divergences of a probability with a probability of reference which is not the uniform probability distribution over the ensemble. In general, it is the average over a subset of probability distribution functions which are consider to be either the ''initial'' of ''final'' states of interest. Let P ave be such an average, then the M-Statistical Complexity of a probability distribution function P (j) , given a P ave of reference, is given by C (M) P (j) Â Ã~H P (j) Â Ã : J s P (j) ,P ave Â Ã An illutrative example. In order to discuss a relatively simple example that can intuitively provide a grasp of the basic mathematical principles of Information Theory we present a hypothetical ''gene expression'' dataset involving four samples each with the expression of five unique probes corresponding to five genes (not necessarily different) as follows in Table 3 .