Probabilistic Turing Machines

Probabilistic Turing Machine is a computational model that extends the deterministic Turing machine by incorporating a probabilistic transition function, enabling it to choose among possible transitions according to a defined probability distribution, thereby allowing for randomized computation.

51

Publications

8K

Citations

84

Authors

51

Institutions

Publications per year

2017–2026

3

Authors

84

  1. JŠ
    Janoš Šimon

    Pennsylvania State University, United States

    5 publications · 228 citations

    5

    h-index

  2. JG
    John Gill

    Stanford University, United States

    4 publications · 1.1K citations

    4

    h-index

  3. RF
    Rūsiņš Freivalds

    University of Latvia, Latvia

    3 publications · 150 citations

    3

    h-index

  4. RB
    Richard Beigel

    Yale University, United States

    3 publications · 275 citations

    3

    h-index

  5. UV
    Umesh Vazirani

    University of California, Berkeley, United States

    3 publications · 2.9K citations

    3

    h-index

1–5 of 84

Venues

  1. SIAM Journal on Computing

    6 publications · 4.1K citations

    6

    h-index

  2. Theoretical Computer Science

    4 publications · 96 citations

    4

    h-index

  3. 2

    h-index

  4. Journal of Computer and System Sciences

    2 publications · 130 citations

    2

    h-index

  5. Proceedings of the American Mathematical Society

    PROC · 2 publications · 56 citations

    2

    h-index

1–5