Universal Convergence of Semimeasures on Individual Random Sequences
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]
|Collections||ANU Research Publications|
|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)|
|Hutter and Muchnik Universal Convergence of Semimeasures 2004.pdf||246.63 kB||Adobe PDF|
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