Bayesian Compartmental Inference

Bayesian Compartmental Inference is a statistical inference framework that integrates Bayesian hierarchical modeling with compartmental dynamical systems to estimate uncertain parameters and latent states in structured population models while quantifying epistemic and aleatoric uncertainty.

3

Publications

181

Citations

13

Authors

8

Institutions

Publications per year

2017–2026

0

Authors

13

  1. FD
    F. de Wolf

    University of Amsterdam, Netherlands

    1 publications · 123 citations

    1

    h-index

  2. JM
    James M. Bower

    California Institute of Technology, United States

    1 publications · 34 citations

    1

    h-index

  3. MV
    Michael Vanier

    California Institute of Technology, United States

    1 publications · 34 citations

    1

    h-index

  4. FY
    Frédéric Y. Bois

    Institut national de l'environnement industriel et des risques, France

    1 publications · 24 citations

    1

    h-index

  5. HP
    Hein Putter

    Leiden University Medical Center, Netherlands

    1 publications · 123 citations

    1

    h-index

1–5 of 13

Venues

  1. Statistics in Medicine

    1 publications · 123 citations

    1

    h-index

  2. Journal of Computational Neuroscience

    1 publications · 34 citations

    1

    h-index

  3. 1

    h-index

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