On sequence prediction for arbitrary measures

Daniil Ryabko*, Marcus Hutter

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    Abstract

    Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which predicts all of the measures in the class? To address this problem, we find some conditions on local absolute continuity which are sufficient for prediction and generalize several different notions that are known to be sufficient for prediction. We also formulate some open questions to outline a direction for finding the conditions on classes of measures for which prediction is possible.

    Original languageEnglish
    Title of host publicationProceedings - 2007 IEEE International Symposium on Information Theory, ISIT 2007
    Pages2346-2350
    Number of pages5
    DOIs
    Publication statusPublished - 2007
    Event2007 IEEE International Symposium on Information Theory, ISIT 2007 - Nice, France
    Duration: 24 Jun 200729 Jun 2007

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    ISSN (Print)2157-8101

    Conference

    Conference2007 IEEE International Symposium on Information Theory, ISIT 2007
    Country/TerritoryFrance
    CityNice
    Period24/06/0729/06/07

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