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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.
Über die Autorin bzw. den Autor: Dr. Daniil Ryabko (HDR) has a full-time position at INRIA, he has recently been on research assignments in Belize and Madagascar.
Titel: Universal Time-Series Forecasting with ...
Verlag: Springer
Erscheinungsdatum: 2020
Einband: Softcover
Zustand: Brand New