The author considers the problem of sequential probability
forecasting in the most general setting, where the observed data
may exhibit an arbitrary form of stochastic dependence. All the
results presented are theoretical, but they concern the foundations
of some problems in such applied areas as machine learning,
information theory and data compression.
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