Universal prediction

Neri Merhav*, Meir Feder

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper consists of an overview on universal prediction from an information-theoretic perspective. Special attention is given to the notion of probability assignment under the self-information loss function, which is directly related to the theory of universal data compression. Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with emphasis on the analogy and the differences between results in the two settings.

Original languageEnglish
Pages (from-to)2124-2147
Number of pages24
JournalIEEE Transactions on Information Theory
Volume44
Issue number6
DOIs
StatePublished - 1998

Funding

FundersFunder number
Israel Academy of Sciences and Humanities
Israel Science Foundation

    Keywords

    • Bayes envelope
    • Entropy
    • Finite-state machine
    • Linear prediction
    • Loss function
    • Probability assignment
    • Redundancy-capacity
    • Stochastic complexity
    • Universal coding
    • Universal prediction

    Fingerprint

    Dive into the research topics of 'Universal prediction'. Together they form a unique fingerprint.

    Cite this