Markov chains are a powerful probability tool of prediction to behavior of sundry systems. For example, staff management for attendance in stores (1); in physic sciences, they provide a model with global properties of local interactions, following Aharonov et al article (2). The applications are several; also the PageRank models, one fundamental application to web search engines most smart, like the Google (3, 4). Nevertheless, the focus of this work is the attendance models of people in establishments, like a banks, public repartitions, airports, for example, or attendance by phone, by using Markov chains, as suggested by work title. Interesting also to note there is a manifold of different situations that may to be described with the same model. The work is divided in two grander parts, the first remembers basic knowledge of probability and a little of linear algebra; only the necessary. It is not strictly necessary the reading of part 1; though, in case of doubts or difficulties a quick consultation is likely to help. The work contribution is in estimating the posterior queue’s size, after a time given t, on mentioned situations, with computational help of a program built in C language, for this search work. Too, it was evaluated the algorithm’s computational complexity.
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