@inproceedings{b99b38b750ea4d20b98e75449e8972c7,
title = "On the foundations of universal sequence prediction",
abstract = "Solomonoff 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.",
author = "Marcus Hutter",
year = "2006",
doi = "10.1007/11750321\_39",
language = "English",
isbn = "3540340211",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "408--420",
booktitle = "Theory and Applications of Models of Computation - Third International Conference, TAMC 2006, Proceedings",
address = "Germany",
note = "3rd International Conference on Theory and Applications of Models of Computation, TAMC 2006 ; Conference date: 15-05-2006 Through 20-05-2006",
}