Markov-process Approximation

Markov-Process Approximation is a stochastic modeling technique that belongs to the class of heavy-traffic approximations, specifically characterized by the use of a continuous-time Markov process to approximate the dynamics of a queueing system under high load conditions.

861

Publications

46.8K

Citations

1.8K

Authors

732

Institutions

Publications per year

2017–2026

148

Authors

1.8K

  1. MW
    Mengdi Wang

    Princeton University, United States

    7 publications · 238 citations

    7

    h-index

  2. HS
    Haruo Sato

    Tohoku University, Japan

    7 publications · 165 citations

    7

    h-index

  3. HG
    Harry G. Perros

    North Carolina State University, United States

    6 publications · 221 citations

    6

    h-index

  4. MZ
    Michele Zorzi

    University of Padua, Italy

    6 publications · 807 citations

    6

    h-index

  5. SG
    Song Guo

    Hong Kong Polytechnic University, Hong Kong SAR China

    6 publications · 291 citations

    6

    h-index

1–5 of 1.8K

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 957 citations

    18

    h-index

  2. IEEE Transactions on Communications

    22 publications · 1.2K citations

    17

    h-index

  3. Operations Research

    16 publications · 1.7K citations

    15

    h-index

  4. IEEE Transactions on Vehicular Technology

    13 publications · 570 citations

    13

    h-index

  5. Stochastic Processes and their Applications

    14 publications · 955 citations

    12

    h-index

1–5