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## On Generalized Computable Universal Priors and their Convergence

Author:Marcus Hutter (2003) Comments:24 pages Subj-class:Probability Theory; Compexity; Learning Report-no:IDSIA-05-05 and cs.LG/0503026 Paper:LaTeX - PostScript - PDF - Html/Gif Slides:PostScript - PDF

Keywords:Sequence prediction; Algorithmic Information Theory; Solomonoff's prior; universal probability; mixture distributions; posterior convergence; computability concepts; Martin-Löf randomness.

Abstract:Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the posterior of the universal semimeasure M converges rapidly to the true sequence generating posterior mu, if the latter is computable. Hence, M is eligible as a universal predictor in case of unknown mu. The first part of the paper investigates the existence and convergence of computable universal (semi)measures for a hierarchy of computability classes: recursive, estimable, enumerable, and approximable. For instance, M is known to be enumerable, but not estimable, and to dominate all enumerable semimeasures. We present proofs for discrete and continuous semimeasures. The second part investigates more closely the types of convergence, possibly implied by universality: in difference and in ratio, with probability 1, in mean sum, and for Martin-Loef random sequences. We introduce a generalized concept of randomness for individual sequences and use it to exhibit difficulties regarding these issues. In particular, we show that convergence fails (holds) on generalized-random sequences in gappy (dense) Bernoulli classes.

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@Article{Hutter:05unipriorx, author = "M. Hutter", number = "IDSIA-05-05", title = "On Generalized Computable Universal Priors and their Convergence", year = "2005", url = "http://www.hutter1.net/ai/unipriorx.htm", http = "http://arxiv.org/abs/cs.LG/0503026", ftp = "ftp://ftp.idsia.ch/pub/techrep/IDSIA-05-05.pdf", keywords = "Sequence prediction; Algorithmic Information Theory; Solomonoff's prior; universal probability; mixture distributions; posterior convergence; computability concepts; Martin-Loef randomness.", abstract = "Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the posterior of the universal semimeasure M converges rapidly to the true sequence generating posterior mu, if the latter is computable. Hence, M is eligible as a universal predictor in case of unknown mu. The first part of the paper investigates the existence and convergence of computable universal (semi)measures for a hierarchy of computability classes: recursive, estimable, enumerable, and approximable. For instance, M is known to be enumerable, but not estimable, and to dominate all enumerable semimeasures. We present proofs for discrete and continuous semimeasures. The second part investigates more closely the types of convergence, possibly implied by universality: in difference and in ratio, with probability 1, in mean sum, and for Martin-Loef random sequences. We introduce a generalized concept of randomness for individual sequences and use it to exhibit difficulties regarding these issues. In particular, we show that convergence fails (holds) on generalized-random sequences in gappy (dense) Bernoulli classes.", }

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