Supplementary Lecture I Tail Bounds

    Abstract

    In probabilistic analysis, we often need to bound the probability that a random variable deviates far from its mean. There are various formulas for this purpose. These are called tail bounds. The weakest of these is the Markov bound , which states that for any nonnegative random variable X with mean µ = EX, Pr(X ≥ k) ≤ µ/k. (Miscellaneous Exercise 83). A… (More)

    Topics

    Figures and Tables

    Sorry, we couldn't extract any figures or tables for this paper.

    Slides referencing similar topics