Tolerance Calibration is a methodological framework in Approximate Bayesian Computation that systematically determines the threshold for accepting simulated data by balancing computational efficiency against inferential accuracy in the approximation of posterior distributions.
15
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2.8K
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28
Authors
21
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Publications per year
2017–2026
2
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15
1–5 of 15
Authors
28
- DH
3
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1
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1
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- CB
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- PBPhilippe Besnier
Institut d'Électronique et des Technologies du numéRique, France
1 publications · 11 citations
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Institutions
21
Oakland University
Rochester, United States
3 h-index
3 publications
284 citations
Fraunhofer-Gesellschaft
Munich, Germany
1 h-index
1 publications
11 citations
University of British Columbia
Vancouver, Canada
1 h-index
2 publications
202 citations
Memorial Sloan Kettering Cancer Center
New York, United States
1 h-index
2 publications
2.5K citations
Institut d'Électronique et des Technologies du numéRique
Rennes, France
1 h-index
3 publications
33 citations
Mallinckrodt (United States)
Hazelwood, United States
1 h-index
1 publications
1.3K citations
Virginia Tech
Blacksburg, United States
1 h-index
1 publications
769 citations
University of Exeter
Exeter, United Kingdom
1 h-index
1 publications
13 citations
1–8 of 21
Venues
- Journal of Quality Technology
3 publications · 284 citations
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- IEEE Robotics and Automation Letters
1 publications · 101 citations
1
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- International Journal of Radiation Oncology*Biology*Physics
1 publications · 1.3K citations
1
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- Statistical Applications in Genetics and Molecular Biology
SAGMB · 1 publications · 265 citations
1
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- Computer Methods in Applied Mechanics and Engineering
1 publications · 769 citations
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