Random Matrix Distributions

Random Matrix Distributions is a class of probability distributions that model the statistical behavior of matrices whose entries are random variables, distinguished by their focus on the joint distribution of eigenvalues, eigenvectors, and matrix invariants arising from symmetries in random matrix ensembles.

814

Publications

60.7K

Citations

1K

Authors

559

Institutions

Publications per year

2017–2026

88

Authors

1K

  1. HY
    Horng‐Tzer Yau

    Harvard University, United States

    18 publications · 1.9K citations

    17

    h-index

  2. PJ
    Peter J. Forrester

    The University of Melbourne, Australia

    17 publications · 1.4K citations

    16

    h-index

  3. LE
    László Erdős

    Institute of Science and Technology Austria, Austria

    18 publications · 1.8K citations

    15

    h-index

  4. SN
    Satya N. Majumdar

    Centre National de la Recherche Scientifique, France

    14 publications · 1.1K citations

    14

    h-index

  5. YV
    Yan V. Fyodorov

    Petersburg Nuclear Physics Institute, Russia

    14 publications · 734 citations

    13

    h-index

1–5 of 1K

Venues

  1. Physical Review Letters

    PRL · 34 publications · 3.3K citations

    26

    h-index

  2. Journal of Multivariate Analysis

    32 publications · 2.1K citations

    23

    h-index

  3. The Annals of Probability

    28 publications · 3.2K citations

    22

    h-index

  4. Journal of Physics A Mathematical and General

    28 publications · 1.5K citations

    21

    h-index

  5. Physical Review E

    PRE · 26 publications · 1.5K citations

    19

    h-index

1–5