Chernoff Bound

Chernoff Bound is a probability inequality that provides exponentially decreasing upper bounds on the tail probabilities of sums of independent random variables, distinguishing itself from other concentration inequalities by its reliance on the moment-generating function to derive tight bounds.

378

Publications

22.6K

Citations

719

Authors

330

Institutions

Publications per year

2017–2026

46

Variants

Chernoff Inequality

Authors

719

  1. JO
    J.J. O’Reilly

    University College London, United Kingdom

    8 publications · 179 citations

    8

    h-index

  2. AP
    A.J. Phillips

    University of Nottingham, United Kingdom

    5 publications · 187 citations

    5

    h-index

  3. LM
    L.B. Milstein

    University of California San Diego, United States

    5 publications · 244 citations

    5

    h-index

  4. FN
    Frank Nielsen

    Sony Computer Science Laboratories, Japan

    4 publications · 208 citations

    4

    h-index

  5. RZ
    Rico Zenklusen

    ETH Zurich, Switzerland

    4 publications · 361 citations

    4

    h-index

1–5 of 719

Venues

  1. IEEE Transactions on Communications

    25 publications · 1.2K citations

    18

    h-index

  2. IEEE Transactions on Information Theory

    23 publications · 1.4K citations

    17

    h-index

  3. arXiv (Cornell University)

    Repository · 13 publications · 408 citations

    12

    h-index

  4. The Annals of Statistics

    9 publications · 458 citations

    9

    h-index

  5. Journal of Mathematical Physics

    9 publications · 448 citations

    9

    h-index

1–5