On the Foundations of Universal Sequence Prediction

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dc.creatorHutter, Marcus
dc.date2006-05-03
dc.date.accessioned2026-07-07T08:17:24Z
dc.date.available2026-07-07T08:17:24Z
dc.descriptionSolomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. We show that Solomonoff's model possesses many desirable properties: Fast convergence and strong bounds, and in contrast to most classical continuous prior densities has no zero p(oste)rior problem, i.e. can confirm universal hypotheses, is reparametrization and regrouping invariant, and avoids the old-evidence and updating problem. It even performs well (actually better) in non-computable environments.
dc.description14 pages
dc.identifierhttps://arxiv.org/abs/cs/0605009
dc.identifierhttp://arxiv.org/abs/cs/0605009
dc.identifierProc. 3rd Annual Conference on Theory and Applications of Models of Computation (TAMC 2006) pages 408-420
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/134079
dc.subjectMachine Learning
dc.subjectInformation Theory
dc.subjectStatistics Theory
dc.titleOn the Foundations of Universal Sequence Prediction
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