Bayesian Multi-state Models

Bayesian Multi-State Models is a statistical modeling framework that belongs to the class of survival analysis methods, distinguished by its incorporation of multiple transient and absorbing states over time and its use of Bayesian inference to estimate transition intensities and state occupancy probabilities under uncertainty.

79

Publications

2.9K

Citations

303

Authors

187

Institutions

Publications per year

2017–2026

23

Authors

303

  1. AR
    Anders Ringgaard Kristensen

    University of Copenhagen, Denmark

    3 publications · 104 citations

    3

    h-index

  2. MV
    Mafalda Viana

    University of Glasgow, United Kingdom

    2 publications · 62 citations

    2

    h-index

  3. JM
    Jason Matthiopoulos

    University of Glasgow, United Kingdom

    2 publications · 89 citations

    2

    h-index

  4. ID
    Ian D. Jonsen

    Macquarie University, Australia

    2 publications · 223 citations

    2

    h-index

  5. TA
    Thomas Algot Søllested

    2 publications · 78 citations

    2

    h-index

1–5 of 303

Venues

  1. Reliability Engineering & System Safety

    6 publications · 175 citations

    6

    h-index

  2. Biometrics

    3 publications · 42 citations

    3

    h-index

  3. Oikos

    3 publications · 118 citations

    3

    h-index

  4. PLoS ONE

    3 publications · 86 citations

    3

    h-index

  5. Livestock Production Science

    2 publications · 78 citations

    2

    h-index

1–5