Predicting non-stationary processes
Copyright policy )Predicting non-stationary processes
The authors are concerned with a problem of prediction of a probability measure given another one, when both are living on the space of one-way infinite finite-alphabet sequences. This means, if a sequence was considered to be generated by the first measure, when do its conditional probabilities w.r.t. the second reference measure converge in some sense. The main result are sufficient local absolute continuity criteria assuring prediction and the generalization of several notions to identify classes of measures for which prediction turns out to be feasible.
330, non-stationary measures, Distribution theory, 510, 519, [MATH.MATH-IT] Mathematics [math]/Information Theory [math.IT], Local absolute continuity, Absolute/KL divergence, Mixtures of measures, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Non-stationary measures, Prediction theory (aspects of stochastic processes), Average/expected criteria, Probability distributions Absolute/KL divergence, Problem solving, Applied Mathematics, [MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT], local absolute continuity, mixture of measures, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], absolute/KL divergence, sequence prediction, Keywords: Convergence of numerical methods, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], average/expected criteria, Sequence prediction, [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT]
330, non-stationary measures, Distribution theory, 510, 519, [MATH.MATH-IT] Mathematics [math]/Information Theory [math.IT], Local absolute continuity, Absolute/KL divergence, Mixtures of measures, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Non-stationary measures, Prediction theory (aspects of stochastic processes), Average/expected criteria, Probability distributions Absolute/KL divergence, Problem solving, Applied Mathematics, [MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT], local absolute continuity, mixture of measures, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], absolute/KL divergence, sequence prediction, Keywords: Convergence of numerical methods, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], average/expected criteria, Sequence prediction, [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT]
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