Markov Multi-state Models

Markov Multi-State Models is a stochastic process model that describes the evolution of a system over time through a finite set of discrete states, governed by the Markov property which assumes that the future state depends only on the current state and not on the sequence of prior states.

314

Publications

19.4K

Citations

1K

Authors

534

Institutions

Publications per year

2017–2026

88

Authors

1K

  1. VS
    Vijay S. Pande

    Stanford University, United States

    18 publications · 2.5K citations

    16

    h-index

  2. GR
    Gregory R. Bowman

    Washington University in St. Louis, United States

    10 publications · 2K citations

    10

    h-index

  3. XH
    Xuhui Huang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    9 publications · 641 citations

    9

    h-index

  4. KA
    Kyle A. Beauchamp

    Stanford University, United States

    6 publications · 1.3K citations

    6

    h-index

  5. FK
    Fu Kit Sheong

    Hong Kong University of Science and Technology, Hong Kong SAR China

    5 publications · 167 citations

    5

    h-index

1–5 of 1K

Venues

  1. The Journal of Chemical Physics

    20 publications · 1.5K citations

    17

    h-index

  2. Journal of Chemical Theory and Computation

    JCTC · 14 publications · 1.6K citations

    13

    h-index

  3. Reliability Engineering & System Safety

    9 publications · 399 citations

    9

    h-index

  4. Statistical Methods in Medical Research

    8 publications · 835 citations

    8

    h-index

  5. IEEE Transactions on Reliability

    7 publications · 202 citations

    7

    h-index

1–5