Bayesian Dynamics Models

Bayesian Dynamics Models is a probabilistic modeling framework that characterizes robot system dynamics by representing uncertainty in the transition function through Bayesian inference, thereby enabling principled uncertainty quantification and robust decision-making under model uncertainty.

14

Publications

616

Citations

49

Authors

35

Institutions

Publications per year

2017–2026

10

Authors

49

  1. BK
    Brennan Klein

    Northeastern University, United States

    2 publications · 123 citations

    2

    h-index

  2. KF
    Karl Friston

    University College London, United Kingdom

    2 publications · 123 citations

    2

    h-index

  3. CH
    Conor Heins

    University College London, United Kingdom

    2 publications · 123 citations

    2

    h-index

  4. MW
    Michael W. Dusenberry

    Google (United States), United States

    2 publications · 87 citations

    2

    h-index

  5. LD
    Lancelot Da Costa

    University College London, United Kingdom

    2 publications · 123 citations

    2

    h-index

1–5 of 49

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 114 citations

    4

    h-index

  2. 1

    h-index

  3. Fisheries Research

    1 publications · 55 citations

    1

    h-index

  4. Interface Focus

    1 publications · 112 citations

    1

    h-index

  5. 1

    h-index

1–5