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
Parents
14
On Bayesian mechanics: a physics of and by beliefs
Maxwell J. D. Ramstead, Dalton A R Sakthivadivel, Conor Heins et al. · Interface Focus · 2023 · 112 citations · Full text
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk et al. · arXiv (Cornell University) · 2018 · 49 citations · Full text
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk et al. · arXiv (Cornell University) · 2019 · 38 citations · Full text
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35

Imperial College London
London, United Kingdom
2 h-index
2 publications
150 citations

Kanagawa University
Yokohama, Japan
1 h-index
1 publications
29 citations
University of Washington
Seattle, United States
1 h-index
2 publications
110 citations
Hasso Plattner Institute
Potsdam, Germany
1 h-index
1 publications
38 citations
Université Libre de Bruxelles
Brussels, Belgium
1 h-index
1 publications
29 citations

University of Exeter
Exeter, United Kingdom
1 h-index
4 publications
132 citations
Virginia Commonwealth University
Richmond, United States
1 h-index
1 publications
16 citations
Stony Brook University
Stony Brook, United States
1 h-index
1 publications
112 citations
1–8 of 35
Repository · 4 publications · 114 citations
4
h-index
1 publications · 16 citations
1
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1 publications · 55 citations
1
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1 publications · 112 citations
1
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1 publications · 29 citations
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