Branching Markov Chains

Branching Markov Chains is a stochastic process that extends the classical Markov chain by allowing each state to generate a random number of offspring particles, each evolving independently according to the same transition mechanism, thereby combining the branching structure of population dynamics with the memoryless state transitions of Markovian processes.

256

Publications

13.6K

Citations

306

Authors

212

Institutions

Publications per year

2017–2026

13

Authors

306

  1. VV
    Vladimir Vatutin

    Steklov Mathematical Institute, Russia

    11 publications · 208 citations

    11

    h-index

  2. AG
    Anthony G. Pakes

    The University of Western Australia, Australia

    7 publications · 278 citations

    7

    h-index

  3. PJ
    Peter Jagers

    Chalmers University of Technology, Sweden

    5 publications · 282 citations

    5

    h-index

  4. KB
    Krishna B. Athreya

    Iowa State University, United States

    5 publications · 568 citations

    5

    h-index

  5. ЕB
    Е. B. Yarovaya

    Lomonosov Moscow State University, Russia

    5 publications · 132 citations

    5

    h-index

1–5 of 306

Venues

  1. Stochastic Processes and their Applications

    34 publications · 1.2K citations

    21

    h-index

  2. Probability Theory and Related Fields

    13 publications · 706 citations

    13

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  3. Advances in Applied Probability

    AAP · 14 publications · 686 citations

    12

    h-index

  4. Journal of Applied Probability

    JAP · 12 publications · 623 citations

    11

    h-index

  5. The Annals of Probability

    11 publications · 602 citations

    11

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1–5