Discrete-time Markov Processes

Discrete-Time Markov Processes is a stochastic process in which the future state depends only on the present state and not on the sequence of preceding states, evolving over discrete time steps according to a fixed set of probabilistic transition rules.

976

Publications

62.7K

Citations

2K

Authors

815

Institutions

Publications per year

2017–2026

191

Variants

Discrete-time Markov Process

Authors

2K

  1. OC
    O.L.V. Costa

    Universidade de São Paulo, Brazil

    10 publications · 463 citations

    10

    h-index

  2. GY
    George Yin

    Wayne State University, United States

    8 publications · 870 citations

    8

    h-index

  3. FD
    François Dufour

    Institut de Mathématiques de Bordeaux, France

    8 publications · 127 citations

    8

    h-index

  4. LZ
    Lixian Zhang

    Harbin Institute of Technology, China

    7 publications · 1.2K citations

    7

    h-index

  5. JC
    José C. Geromel

    Universidade Estadual de Campinas (UNICAMP), Brazil

    7 publications · 352 citations

    7

    h-index

1–5 of 2K

Venues

  1. IEEE Transactions on Automatic Control

    32 publications · 3K citations

    23

    h-index

  2. Journal of Applied Probability

    JAP · 46 publications · 2K citations

    21

    h-index

  3. IEEE Transactions on Wireless Communications

    18 publications · 1K citations

    17

    h-index

  4. Advances in Applied Probability

    AAP · 21 publications · 1.3K citations

    16

    h-index

  5. IEEE Transactions on Communications

    18 publications · 1.4K citations

    14

    h-index

1–5