Markovian Embedding

Markovian Embedding is a formal framework that extends non-Markovian stochastic processes into higher-dimensional state spaces wherein the augmented process regains the Markov property by embedding historical dependencies into auxiliary variables.

62

Publications

3.7K

Citations

114

Authors

98

Institutions

Publications per year

2017–2026

14

Authors

114

  1. YP
    Yuval Peres

    Microsoft (United States), United States

    4 publications · 185 citations

    4

    h-index

  2. IG
    Igor Goychuk

    University of Augsburg, Germany

    4 publications · 265 citations

    4

    h-index

  3. AN
    Assaf Naor

    Courant Institute of Mathematical Sciences, United States

    3 publications · 160 citations

    3

    h-index

  4. JR
    James R. Lee

    University of Washington, United States

    3 publications · 81 citations

    3

    h-index

  5. PS
    P. Siegle

    University of Augsburg, Germany

    3 publications · 240 citations

    3

    h-index

1–5 of 114

Venues

  1. Physical Review E

    PRE · 4 publications · 167 citations

    4

    h-index

  2. Probability Theory and Related Fields

    3 publications · 54 citations

    3

    h-index

  3. 2

    h-index

  4. Journal of Statistical Physics

    2 publications · 33 citations

    2

    h-index

  5. Physical review. A/Physical review, A

    PRA · 2 publications · 126 citations

    2

    h-index

1–5