2014/12/06 by S. S. Melnik, Melnik, S. S., O. V. Usatenko +1 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Statistical Mechanics (cond-mat.stat-mech) #Statistics and Probability (physics.data-an) #cond-mat.dis-nn #cond-mat.stat-mech #cs.IT #math.IT #physics.data-an
paper · pdf · doi:10.48550/arxiv.1412.3692
9 pages, 3 fifures. arXiv admin note: substantial text overlap with arXiv:1411.2761
arxiv created 2014/12/06 · arxiv updated 2014/12/12
The goal of this paper is to develop an estimate for the entropy of random long-range correlated symbolic sequences with elements belonging to a finite alphabet. As a plausible model, we use the high-order additive stationary ergodic Markov chain. Supposing that the correlations between random elements of the chain are weak we express the differential entropy of the sequence by means of the symbolic pair correlation function. We also examine an algorithm for estimating the differential entropy of finite symbolic sequences. We show that the entropy contains two contributions, the correlation and fluctuation ones. The obtained analytical results are used for numerical evaluation of the entropy of written English texts and DNA nucleotide sequences. The developed theory opens the way for constructing a more consistent and sophisticated approach to describe the systems with strong short- and weak long-range correlations.