Embedded Markov Chain Analysis

Embedded Markov Chain Analysis is a stochastic modeling technique that belongs to the class of Markovian analytical methods, distinguished by its focus on analyzing bulk service queueing systems through the examination of system states at specific, discrete time points—typically instants of customer arrivals or service completions—where transitions are governed by probabilistic rules conditional on the batch processing of customers.

97

Publications

4.1K

Citations

185

Authors

121

Institutions

Publications per year

2017–2026

15

Authors

185

  1. MF
    Marcel F. Neuts

    University of Arizona, United States

    5 publications · 100 citations

    5

    h-index

  2. SG
    Shan Gao

    Fuyang Normal University, China

    3 publications · 55 citations

    3

    h-index

  3. DW
    Dae Won Choi

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 31 citations

    2

    h-index

  4. RQ
    Rose Qingyang Hu

    Utah State University, United States

    2 publications · 61 citations

    2

    h-index

  5. PF
    Pingzhi Fan

    Southwest Jiaotong University, China

    2 publications · 61 citations

    2

    h-index

1–5 of 185

Venues

  1. Advances in Applied Probability

    AAP · 9 publications · 316 citations

    9

    h-index

  2. Journal of Applied Probability

    JAP · 5 publications · 117 citations

    5

    h-index

  3. Operations Research

    4 publications · 277 citations

    4

    h-index

  4. Communications in Statistics Stochastic Models

    4 publications · 211 citations

    4

    h-index

  5. IEEE Transactions on Vehicular Technology

    4 publications · 213 citations

    4

    h-index

1–5