Expected-time Analysis

Expected-Time Analysis is a probabilistic method for evaluating the average-case performance of algorithms, particularly in the context of randomized data structures like skip lists, that characterizes the expected running time as a function of input size under typical probabilistic assumptions governing the structure’s randomization.

237

Publications

12.4K

Citations

447

Authors

251

Institutions

Publications per year

2017–2026

19

Parents

Authors

447

  1. SA
    Steve Alpern

    London School of Economics and Political Science, United Kingdom

    9 publications · 571 citations

    9

    h-index

  2. JB
    Jon Bentley

    Carnegie Mellon University, United States

    4 publications · 815 citations

    4

    h-index

  3. DB
    David B. Shmoys

    Cornell University, United States

    3 publications · 106 citations

    3

    h-index

  4. EA
    Edward Anderson

    University of Cambridge, United Kingdom

    3 publications · 198 citations

    3

    h-index

  5. PW
    Peter Winkler

    Emory University, United States

    3 publications · 360 citations

    3

    h-index

1–5 of 447

Venues

  1. SIAM Journal on Computing

    15 publications · 1.2K citations

    14

    h-index

  2. Operations Research

    9 publications · 269 citations

    9

    h-index

  3. Random Structures and Algorithms

    6 publications · 515 citations

    6

    h-index

  4. Journal of Applied Probability

    JAP · 5 publications · 250 citations

    5

    h-index

  5. Journal of the ACM

    JACM · 5 publications · 226 citations

    5

    h-index

1–5