Information theory

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213814 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 53, NO. 10, OCTOBER 2007 Since the two rate regions are now symmetric (see Fig. 4), the second constraint becomes equality. This expresses  as a function of a, and

3814 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 53, NO. 10, OCTOBER 2007 Since the two rate regions are now symmetric (see Fig. 4), the second constraint becomes equality. This expresses  as a function of a, and

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Source URL: www.winlab.rutgers.edu

Language: English - Date: 2008-01-29 12:19:26
    22V. G. VOVK ON A CRITERION OF RANDOMNESS (Communicated by Academician A. N. Kolmogorov, This work belongs to algorithmic information theory (seeTheorem 1 shows that if some sequence is random with resp

    V. G. VOVK ON A CRITERION OF RANDOMNESS (Communicated by Academician A. N. Kolmogorov, This work belongs to algorithmic information theory (seeTheorem 1 shows that if some sequence is random with resp

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    Source URL: www.vovk.net

    Language: English - Date: 2009-01-29 05:02:58
      232009 IEEE Information Theory Workshop Mutual Information and Posterior Estimates in Channels of Exponential Family Type Maxim Raginsky

      2009 IEEE Information Theory Workshop Mutual Information and Posterior Estimates in Channels of Exponential Family Type Maxim Raginsky

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      Source URL: maxim.ece.illinois.edu

      Language: English - Date: 2012-04-18 12:07:52
        24to be published in: Kurt A Richardson (ed): Managing the Complex: Philosophy, Theory, and Practice, (Institute for the Study of Coherence and Emergence/Information Age Publishing) How can we think the complex? C

        to be published in: Kurt A Richardson (ed): Managing the Complex: Philosophy, Theory, and Practice, (Institute for the Study of Coherence and Emergence/Information Age Publishing) How can we think the complex? C

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        Source URL: cleamc11.vub.ac.be

        Language: English - Date: 2014-05-02 12:27:42
          253F1 Information Theory Course (supervisor copy) 1 3F1 Information Theory Course Nick Kingsbury

          3F1 Information Theory Course (supervisor copy) 1 3F1 Information Theory Course Nick Kingsbury

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          Source URL: sigproc.eng.cam.ac.uk

          Language: English - Date: 2013-08-05 15:50:49
            26Ann. N.Y. Acad. Sci. ISSNANNALS OF THE NEW YORK ACADEMY OF SCIENCES Issue: The Year in Evolutionary Biology The use of information theory in evolutionary biology

            Ann. N.Y. Acad. Sci. ISSNANNALS OF THE NEW YORK ACADEMY OF SCIENCES Issue: The Year in Evolutionary Biology The use of information theory in evolutionary biology

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            Source URL: adamilab.msu.edu

            Language: English - Date: 2012-06-20 23:31:11
              27Systems and Information Division Technical Committee on Discrete Event Systems Toward a New Framework for Dependable Systems Goal Theory for analysis, design and control of the Discrete Event Systems (DES)

              Systems and Information Division Technical Committee on Discrete Event Systems Toward a New Framework for Dependable Systems Goal Theory for analysis, design and control of the Discrete Event Systems (DES)

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              Source URL: www.sice.jp

              Language: English - Date: 2011-09-11 22:03:30
                28Engineering Tripos Part IIA THIRD YEAR 3F1: Signals and Systems INFORMATION THEORY

                Engineering Tripos Part IIA THIRD YEAR 3F1: Signals and Systems INFORMATION THEORY

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                Source URL: www-sigproc.eng.cam.ac.uk

                Language: English - Date: 2013-08-05 15:50:33
                  29Network Theory III: Bayesian Networks, Information and Entropy John Baez, Brendan Fong, Tobias Fritz, Tom Leinster Given finite sets X and Y , a stochastic map f : X Y assigns a

                  Network Theory III: Bayesian Networks, Information and Entropy John Baez, Brendan Fong, Tobias Fritz, Tom Leinster Given finite sets X and Y , a stochastic map f : X Y assigns a

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                  Source URL: math.ucr.edu

                  Language: English - Date: 2014-03-17 09:51:23
                    30Proving Lower bounds using Information Theory Frédéric Magniez CNRS, Univ Paris Diderot

                    Proving Lower bounds using Information Theory Frédéric Magniez CNRS, Univ Paris Diderot

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                    Source URL: www.irif.fr

                    Language: English - Date: 2017-10-25 10:04:25