Large Deviations For Markov Chains

Large Deviations for Markov Chains is a theoretical framework within probability theory that characterizes the exponential decay rates of probabilities of rare events associated with empirical measures or trajectories of Markov chains, distinguished from other large deviation principles by its focus on dependence structures arising from Markovian dynamics.

338

Publications

23.2K

Citations

417

Authors

307

Institutions

Publications per year

2017–2026

39

Authors

417

  1. RS
    Richard S. Ellis

    California Institute of Technology, United States

    9 publications · 1.6K citations

    9

    h-index

  2. PD
    Paul Dupuis

    Brown University, United States

    8 publications · 1.2K citations

    8

    h-index

  3. OZ
    Ofer Zeitouni

    Technion – Israel Institute of Technology, Israel

    7 publications · 4.3K citations

    7

    h-index

  4. AA
    A. A. Mogulskiĭ

    Sobolev Institute of Mathematics, Russia

    7 publications · 355 citations

    7

    h-index

  5. HT
    Hugo Touchette

    Queen Mary University of London, United Kingdom

    6 publications · 2.6K citations

    6

    h-index

1–5 of 417

Venues

  1. Stochastic Processes and their Applications

    30 publications · 1.2K citations

    20

    h-index

  2. The Annals of Probability

    22 publications · 1.6K citations

    19

    h-index

  3. Probability Theory and Related Fields

    28 publications · 1.2K citations

    19

    h-index

  4. Journal of Statistical Physics

    19 publications · 1.2K citations

    16

    h-index

  5. The Annals of Applied Probability

    12 publications · 399 citations

    11

    h-index

1–5