Ergodic Decomposition

Ergodic Decomposition is a foundational result in ergodic theory that uniquely expresses a dynamical system's invariant measure as a convex combination of ergodic measures, thereby decomposing the system into its indecomposable ergodic components.

81

Publications

2.8K

Citations

112

Authors

94

Institutions

Publications per year

2017–2026

11

Authors

112

  1. RM
    Robert M. Gray

    Stanford University, United States

    4 publications · 182 citations

    4

    h-index

  2. TT
    Terence Tao

    University of California, Los Angeles, United States

    3 publications · 153 citations

    3

    h-index

  3. MW
    Minmin Wang

    Laboratoire de Probabilités, Statistique et Modélisation, France

    2 publications · 23 citations

    2

    h-index

  4. LD
    L. Davisson

    University of Southern California, United States

    2 publications · 134 citations

    2

    h-index

  5. AS
    Alexander Starkov

    Lomonosov Moscow State University, Russia

    2 publications · 25 citations

    2

    h-index

1–5 of 112

Venues

  1. Ergodic Theory and Dynamical Systems

    8 publications · 226 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 87 citations

    5

    h-index

  3. IEEE Transactions on Information Theory

    5 publications · 199 citations

    5

    h-index

  4. Transactions of the American Mathematical Society

    TRAN · 4 publications · 307 citations

    4

    h-index

  5. Journal of Applied Probability

    JAP · 3 publications · 112 citations

    3

    h-index

1–5