  Stochastic chains with memory of variable length constitute an interesting
family of stochastic chains of infinite order on a finite alphabet. The idea is
that for each past, only a finite suffix of the past, called context, is enough
to predict the next symbol. These models were first introduced in the
information theory literature by Rissanen (1983) as a universal tool to perform
data compression. Recently, they have been used to model up scientific data in
areas as different as biology, linguistics and music. This paper presents a
personal introductory guide to this class of models focusing on the algorithm
Context and its rate of convergence.
