Abstract
The Shannon entropy is a standard measure for the order state of symbol sequences, such as, for example, DNA sequences. In order to incorporate correlations between symbols, the entropy of n-mers (consecutive strands of n symbols) has to be determined. Here, an assay is presented to estimate such higher order entropies (block entropies) for DNA sequences when the actual number of observations is small compared with the number of possible outcomes. The n-mer probability distribution underlying the dynamical process is reconstructed using elementary statistical principles: The theorem of asymptotic equi-distribution and the Maximum Entropy Principle. Constraints are set to force the constructed distributions to adopt features which are characteristic for the real probability distribution. From the many solutions compatible with these constraints the one with the highest entropy is the most likely one according to the Maximum Entropy Principle. An algorithm performing this procedure is expounded. It is tested by applying it to various DNA model sequences whose exact entropies are known. Finally, results for a real DNA sequence, the complete genome of the Epstein Barr virus, are presented and compared with those of other information carriers (texts, computer source code, music). It seems as if DNA sequences possess much more freedom in the combination of the symbols of their alphabet than written language or computer source codes.