Maximizing Entropy of Pickard Random Fields for 2x2 Binary Constraints

Jacob Søgaard, Søren Forchhammer

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    Abstract

    This paper considers the problem of maximizing the entropy of two-dimensional (2D) Pickard Random Fields (PRF) subject to constraints. We consider binary Pickard Random
    Fields, which provides a 2D causal finite context model and use it to define stationary probabilities for 2x2 squares, thus allowing us to calculate the entropy of the field. All possible binary 2x2 constraints are considered and all constraints are categorized into groups according to their properties. For constraints which can be modeled by a PRF approach and with positive entropy, we characterize and provide statistics of the maximum PRF entropy. As examples, we consider the well known hard square constraint along with a few other constraints.
    Original languageEnglish
    Title of host publicationProceedings of 2014 IEEE International Symposium on Information Theory
    PublisherIEEE
    Publication date2014
    Pages2022-2026
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE International Symposium on Information Theory - Honolulu, United States
    Duration: 30 Jun 20145 Jul 2014

    Conference

    Conference2014 IEEE International Symposium on Information Theory
    Country/TerritoryUnited States
    CityHonolulu
    Period30/06/201405/07/2014

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