Joint Typicality

Joint Typicality is a probabilistic concept in information theory that characterizes the set of sequence pairs jointly generated by a stationary memoryless source-channel system, distinguished by their empirical entropy converging asymptotically to the true joint entropy of the underlying distribution.

14

Publications

451

Citations

29

Authors

24

Institutions

Publications per year

2017–2026

7

Authors

29

  1. MG
    Michael Gastpar

    University of California, Berkeley, United States

    2 publications · 34 citations

    2

    h-index

  2. CF
    Chen Feng

    University of British Columbia, Canada

    2 publications · 34 citations

    2

    h-index

  3. JC
    Jun Chen

    McMaster University, Canada

    2 publications · 54 citations

    2

    h-index

  4. BN
    Bobak Nazer

    Boston University, United States

    2 publications · 34 citations

    2

    h-index

  5. SH
    Sung Hoon Lim

    Korea Institute of Ocean Science and Technology, South Korea

    2 publications · 34 citations

    2

    h-index

1–5 of 29

Venues

  1. IEEE Transactions on Information Theory

    3 publications · 266 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 34 citations

    2

    h-index

  3. Sadhana

    1 publications · 14 citations

    1

    h-index

  4. IEEE Transactions on Wireless Communications

    1 publications · 12 citations

    1

    h-index

  5. IEEE Transactions on Communications

    1 publications · 11 citations

    1

    h-index

1–5