Zipf and non-Zipf Laws for Homogeneous Markov Chain

Let us consider a homogeneous Markov chain with discrete time and with a finite set of states are nonrecurrent. The goal of this work is to study frequencies of trajectories in this chain, i.e., " words " composed of symbols E 1 ,. .. , E n ending with the " space " E 0. Let us order words according to their probabilities ; denote by p(t) the probability of the tth word in this list. In this paper we prove that in a typical case the asymptotics of the function p(t) has a power character, and define its exponent from the matrix of transition probabilities. If this matrix is block-diagonal, then with some specific values of transition probabilities the power asymptotics gets (logarithmic) addends. But if this matrix is rather sparse, then probabilities quickly decrease; namely, the rate of asymptotics is greater than that of the power one, but not greater than that of the exponential one. We also establish necessary and sufficient conditions for the exponential order of decrease and obtain a formula for determining the exponent from the transition probability matrix and the initial distribution vector. Index Terms —– Time-homogeneous Markov chain with a finite state space, power laws, analytic information theory, monkeys typing randomly, exponential laws, rank-frequency distribution.