**M. Malyutov, P. Grosu, and T. Zhang **

*SCOT stationary distribution evaluation for some
examples *

We call Stochastic COntext
Trees (abbreviated as SCOT) n-Markov Chains with every state of a string independent
of the symbols in its more remote past than the **context** of **length**
determined by the preceding symbols of this state. Previous somewhat confusing
names for SCOT were VLMC, PST, CTW. We estimated SCOT
parameters for testing homogeneity of data strings in No. 4, vol. 13 of this
journal. Our more efficient SCOT fitting algorithm will be exposed
elsewhere. Here, we *postulate* SCOT models and study their convergence without fitting
SCOT from data sets. A SCOT *stationary
distribution over contexts* is iteratively evaluated here explicitly in
several examples. Our main tool is a 1-MC generated by the SCOT with the set of
contexts as its state space.

**КЛЮЧЕВЫЕ**** СЛОВА**: Variable Memory Length Markov
Chain, Stochastic Context Trees, Stationary Distribution of Contexts