Higher-order Markov Models

Higher-Order Markov Models is a type of stochastic process model in which the probability distribution of future states depends on a fixed number of preceding states greater than one, thereby extending the first-order Markov property by incorporating longer memory dependencies in state transitions.

158

Publications

8.8K

Citations

343

Authors

224

Institutions

Publications per year

2017–2026

20

Authors

343

  1. MK
    Michael K. Ng

    Hong Kong Baptist University, Hong Kong SAR China

    6 publications · 687 citations

    6

    h-index

  2. JA
    Johan A. du Preez

    Stellenbosch University, South Africa

    4 publications · 78 citations

    4

    h-index

  3. JH
    Jan H. Schumann

    University of Passau, Germany

    3 publications · 220 citations

    3

    h-index

  4. EA
    Eva Anderl

    University of Passau, Germany

    3 publications · 220 citations

    3

    h-index

  5. FV
    Florian von Wangenheim

    ETH Zurich, Switzerland

    3 publications · 220 citations

    3

    h-index

1–5 of 343

Venues

  1. IEEE Transactions on Information Theory

    5 publications · 498 citations

    5

    h-index

  2. PLoS ONE

    4 publications · 193 citations

    4

    h-index

  3. 3

    h-index

  4. BMC Bioinformatics

    3 publications · 181 citations

    3

    h-index

  5. Bioinformatics

    3 publications · 199 citations

    3

    h-index

1–5