Abstract:
Stochastic COntext Tree (abbreviated as SCOT) is m-Markov Chain 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. SCOT has also appeared in other fields under various names (VLMC, PST, CTW) for compression applications. Consistency of algorithms for training SCOT have been derived for stationary time series with mixing.
We survey recent advances in SCOT modeling, parallel training and statistical inference described in chapter 3 of B. Ryabko, J. Astola and M. Malyutov `Compression-Based Methods of Statistical Analysis and Prediction of Time Series', Springer, which is to appear shortly.
Bio:
Mikhail Malyutov, Professor of Applied Statistics, Northeastern University, Boston. On his sabbatical he presented talks in many UK and Australian Universities. Before 1995, he was with Kolmogorov Statistical Lab, Moscow.