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  • Shannon's Information Measures and Markov Structures

    Series Name:

    Sensing, Signals and Communication Seminar

    Presenter:

    Raymond W. Yeung, Institute of Network Coding, The Chinese University of Hong Kong

    Sponsors:

    Information Initiative at Duke (iiD), Computer Science, Electrical and Computer Engineering (ECE), Mathematics, Pratt School of Engineering, and Statistical Science

    Location:
    Gross Hall 304B - Map

    When:

    to

    Contact:

    Peterson, Kathy

    Email:

    kathy.peterson@duke.edu

    Phone:

    613-7829

    In the 1990's, the theory of I-Measure was developed as a full-fledged set-theoretic interpretation of Shannon's information measures. In this talk, we first give an overview of this theory. Then we discuss a set of tools developed on the I-Measure that is most suitable for studying a special Markov structure called full conditional mutual independence (FCMI), which turns out to be a building block for Markov random fields. One application of these tools is to show that the I-Measure of a Markov chain (a special case of a Markov random field) exhibits a very simple structure and is always nonnegative.

    In the last part of the talk, we discuss some recent results along this line: i. a characterization of the Markov structure of a subfield of a Markov random field; ii. the Markov chain being the only Markov random field such that the I-Measure is always nonnegative.

    Lecture/Talk