Markov Chain Composition

Markov Chain Composition is a method of algorithmic composition that generates musical sequences by modeling transitions between musical events as a Markov process, where the probability of each subsequent event depends only on the current state.

42

Publications

1.4K

Citations

76

Authors

61

Institutions

Publications per year

2017–2026

3

Authors

76

  1. GW
    Guang Wu

    University of Udine, Italy

    3 publications · 33 citations

    3

    h-index

  2. MD
    Martin Dyer

    University of Leeds, United Kingdom

    2 publications · 51 citations

    2

    h-index

  3. MC
    Mary Cryan

    University of Leeds, United Kingdom

    2 publications · 51 citations

    2

    h-index

  4. LA
    Leslie Ann Goldberg

    University of Warwick, United Kingdom

    2 publications · 51 citations

    2

    h-index

  5. PB
    Peter Buchholz

    TU Dortmund University, Germany

    2 publications · 87 citations

    2

    h-index

1–5 of 76

Venues

  1. Journal of Applied Probability

    JAP · 2 publications · 62 citations

    2

    h-index

  2. 1

    h-index

  3. Journal of Retailing

    1 publications · 82 citations

    1

    h-index

  4. Cancer Letters

    1 publications · 11 citations

    1

    h-index

  5. Journal of Mathematical Sciences

    1 publications · 15 citations

    1

    h-index

1–5