Probabilistic Refinement

Probabilistic Refinement is a refinement calculus framework that extends classical refinement relations with probabilistic choice, enabling the formal derivation of probabilistic programs by preserving safety and liveness properties under stochastic behavior.

12

Publications

210

Citations

62

Authors

37

Institutions

Publications per year

2017–2026

1

Authors

62

  1. AC
    Argelia Castaño

    Instituto de Salud Carlos III, Spain

    1 publications · 23 citations

    1

    h-index

  2. NP
    Nicole Probst‐Hensch

    University of Basel, Switzerland

    1 publications · 23 citations

    1

    h-index

  3. SF
    Samuel Fuhrimann

    Swiss Tropical and Public Health Institute, Switzerland

    1 publications · 23 citations

    1

    h-index

  4. EG
    Eva Govarts

    Flemish Institute for Technological Research, Belgium

    1 publications · 23 citations

    1

    h-index

  5. IC
    Irene Cattaneo

    European Food Safety Authority, Italy

    1 publications · 23 citations

    1

    h-index

1–5 of 62

Venues

  1. Nordic journal of computing

    2 publications · 29 citations

    2

    h-index

  2. Formal Aspects of Computing

    1 publications · 10 citations

    1

    h-index

  3. IEEE PES Transmission and Distribution Conference and Exhibition

    Conference · 1 publications · 11 citations

    1

    h-index

  4. Journal of Sensors

    1 publications · 16 citations

    1

    h-index

  5. Electronic Notes in Theoretical Computer Science

    ENTCS · 1 publications · 13 citations

    1

    h-index

1–5