Probabilistic Analysis

Probabilistic Analysis is a methodological framework within algorithm analysis that characterizes the expected behavior of algorithms under assumptions about the probability distribution of inputs, distinguishing it from worst-case or average-case deterministic analysis by explicitly incorporating randomness in input modeling.

2.6K

Publications

149.7K

Citations

6.8K

Authors

2.1K

Institutions

Publications per year

2017–2026

677

Authors

6.8K

  1. JA
    Jaume Abella

    Universitat Politècnica de Catalunya, Spain

    14 publications · 629 citations

    13

    h-index

  2. FJ
    Francisco J. Cazorla

    Universitat Politècnica de Catalunya, Spain

    13 publications · 608 citations

    12

    h-index

  3. EQ
    Eduardo Quiñones

    Universitat Politècnica de Catalunya, Spain

    13 publications · 608 citations

    12

    h-index

  4. AS
    Abdul‐Hamid Soubra

    Nantes Université, France

    12 publications · 588 citations

    12

    h-index

  5. YW
    Y.-T. Wu

    Southwest Research Institute, United States

    12 publications · 733 citations

    11

    h-index

1–5 of 6.8K

Venues

  1. Computers and Geotechnics

    33 publications · 1.7K citations

    25

    h-index

  2. Medical Decision Making

    MDM · 28 publications · 4.4K citations

    24

    h-index

  3. Reliability Engineering & System Safety

    26 publications · 2.8K citations

    21

    h-index

  4. Risk Analysis

    28 publications · 2.5K citations

    20

    h-index

  5. Technometrics

    18 publications · 1.2K citations

    16

    h-index

1–5