On semimeasures predicting Martin-Lof random sequences

Date

2007

Authors

Hutter, Marcus
Muchnik, Andrej

Journal Title

Journal ISSN

Volume Title

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

Description

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

Citation

Source

Theoretical Computer Science

Type

Journal article

Book Title

Entity type

Access Statement

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Restricted until

2037-12-31