Adaptive Approximate Bayesian Computation
Adaptive Approximate Bayesian Computation is a methodological extension of Approximate Bayesian Computation that dynamically adjusts algorithmic components—such as tolerance levels, proposal distributions, or kernel weights—during the iterative sampling process to improve computational efficiency and posterior approximation accuracy.
31
Publications
2.8K
Citations
101
Authors
67
Institutions
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2017–2026
18
31
Adaptive approximate Bayesian computation
Mark Beaumont, J. M. Cornuet, Jean‐Michel Marin et al. · Biometrika · 2009 · 622 citations · Full text
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Center of Advanced European Studies and Research, Germany
2 publications · 162 citations
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Center of Advanced European Studies and Research, Germany
2 publications · 162 citations
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67

Queensland University of Technology
Brisbane, Australia
3 h-index
7 publications
855 citations

Imperial College London
London, United Kingdom
3 h-index
7 publications
2K citations

University of Tübingen
Tübingen, Germany
2 h-index
4 publications
92 citations

University of Otago
Dunedin, New Zealand
2 h-index
2 publications
63 citations
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
2 h-index
4 publications
92 citations
Max Planck Society
Munich, Germany
2 h-index
12 publications
972 citations
University of Reading
Reading, United Kingdom
2 h-index
2 publications
649 citations

Université de Montpellier
Montpellier, France
2 h-index
4 publications
850 citations
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1 publications · 21 citations
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1 publications · 33 citations
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1 publications · 53 citations
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1 publications · 36 citations
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1 publications · 249 citations
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