On semimeasures predicting Martin-Lof random sequences
Date
2007
Authors
Hutter, Marcus
Muchnik, Andrej
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Publisher
Elsevier
Abstract
Solomonoff's central result on induction is that the prediction of a universal semimeasure M converges rapidly and with probability 1 to the true sequence generating predictor μ, if the latter is computable. Hence, M is eligible as a universal sequence p
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Keywords
Keywords: Convergence of numerical methods; Data structures; Probability distributions; Problem solving; Algorithmic information theory; Mixture distributions; Predictive convergence; Quasimeasures; Sequence prediction; Supermartingales; Universal enumerable semime Algorithmic information theory; Martin-Löf randomness; Mixture distributions; Predictive convergence; Quasimeasures; Sequence prediction; Supermartingales; Universal enumerable semimeasure
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Source
Theoretical Computer Science
Type
Journal article
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2037-12-31
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