Markov Inequality

Markov Inequality is a fundamental theorem in probability theory that provides an upper bound on the probability that a non-negative random variable exceeds a positive multiple of its expected value, expressed as \( P(X \geq a) \leq \frac{\mathbb{E}[X]}{a} \) for \( a > 0 \).

96

Publications

5.5K

Citations

142

Authors

106

Institutions

Publications per year

2017–2026

9

Variants

Markov's Inequality

Authors

142

  1. PD
    Peter Dörfler

    Montanuniversität Leoben, Austria

    5 publications · 122 citations

    5

    h-index

  2. PB
    Peter Borwein

    Simon Fraser University, Canada

    5 publications · 109 citations

    5

    h-index

  3. TE
    Tamás Erdélyi

    Texas A&M University, United States

    5 publications · 110 citations

    5

    h-index

  4. MB
    Mirosław Baran

    Jagiellonian University, Poland

    3 publications · 64 citations

    3

    h-index

  5. AB
    Albrecht Böttcher

    Chemnitz University of Technology, Germany

    3 publications · 55 citations

    3

    h-index

1–5 of 142

Venues

  1. Journal of Approximation Theory

    17 publications · 516 citations

    13

    h-index

  2. Constructive Approximation

    6 publications · 91 citations

    6

    h-index

  3. Transactions of the American Mathematical Society

    TRAN · 4 publications · 89 citations

    4

    h-index

  4. Journal of the London Mathematical Society

    3 publications · 75 citations

    3

    h-index

  5. Canadian Journal of Mathematics

    3 publications · 38 citations

    3

    h-index

1–5