Applications of Stochastic Context Trees (SCOT) modeling of stationary processes

An overview of SCOT includes its reduction to MC, finding stationary distribution, asymptotic normality of additive transition functions and STATISTICAL THEORY OF ESTIMATION AND TESTING. We apply it for homogeneity testing and develop a hybrid slow Hidden Markov Model switching model with SCOT emissions which might be a realistic model for Genome, analysis of combined authorship of literary works, seismological data or financial time series with piecewise volatility. Our combined online and offline segmentation stage estimates Change Points between times of constant HMM states using homogeneity test for SCOT emissions strings. This can be made recurrently in parallel.