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Universal Convergence of Semimeasures on Individual Random Sequences

Hutter, Marcus; Muchnik, Andrej


Solomonoff’s central result on induction is that the posterior of a universal semimeasure M converges rapidly and with probability 1 to the true sequence generating posterior μ, if the latter is computable. Hence, M is eligible as a universal sequence predictor in case of unknown μ. Despite some nearby results and proofs in the literature, the stronger result of convergence for all (Martin-Löf) random sequences remained open. Such a convergence result would be particularly interesting and...[Show more]

CollectionsANU Research Publications
Date published: 2004
Type: Conference paper
Book Title: Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
DOI: 10.1007/978-3-540-30215-5_19


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