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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.
Algorithms and Programs of Dynamic Mixture Estimation
Probabilistic Forecasting and Bayesian Data Assimilation
Discrete Stochastic Processes
Markov Chains: Theory and Applications
Time-Series Prediction and Applications
Stochastic Processes: Harmonizable Theory
Mathematical Perspectives on Neural Networks
Mixture Models and Applications
Applied Time Series Analysis and Forecasting with Python
Advanced Forecasting with Python
Universal Coding and Order Identification by Model Selection Methods
Markov Chains