Presentation of automata models of Markovian functions on the basis of aggregation of Markov chains

In the paper we present the solution to the problem of algorithmic synthesis of automata models of Markov's functions on the basis of aggregation of finite Markov's chains. Introduced equivalent automata models of Markov's functions. We determined the dependence of the complexity of algorithmic implementation of automata models from size of a stochastic matrix and length of implication vector of this matrix. Matrix describes law of obtaining of aggregated chain. We estimated the complexity of the considered models.