Importance Sampling Approximate Bayesian Computation
Importance Sampling Approximate Bayesian Computation is a methodological framework within approximate Bayesian computation that employs importance sampling to weight candidate parameter values according to their proximity to observed data, thereby enhancing computational efficiency in approximating posterior distributions.
28
Publications
1.9K
Citations
73
Authors
56
Institutions
Publications per year
2017–2026
11
28
Adaptive approximate Bayesian computation
Mark Beaumont, J. M. Cornuet, Jean‐Michel Marin et al. · Biometrika · 2009 · 622 citations · Full text
ABC likelihood-free methods for model choice in Gibbs random fields
Aude Grelaud, Jean‐Michel Marin, Christian P. Robert et al. · Bayesian Analysis · 2009 · 124 citations · Full text
Computing, Biology, Biostatistics +17
1–5 of 28
73
Centre de Recherche en Mathématiques de la Décision, France
2 publications · 746 citations
2
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2
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2
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2
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2
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1–5 of 73
56
Lancaster University
Lancaster, United Kingdom
2 h-index
2 publications
133 citations

Université de Montpellier
Montpellier, France
2 h-index
2 publications
637 citations

Université Paris Dauphine-PSL
Paris, France
2 h-index
2 publications
746 citations

Newcastle University
Newcastle upon Tyne, United Kingdom
2 h-index
2 publications
133 citations
University of Reading
Reading, United Kingdom
2 h-index
2 publications
649 citations

Imperial College London
London, United Kingdom
2 h-index
6 publications
1.9K citations

Universidade Federal do Rio de Janeiro
Rio de Janeiro, Brazil
2 h-index
3 publications
43 citations
Centre de Biologie pour la Gestion des Populations
Montferrier-sur-Lez, France
2 h-index
2 publications
637 citations
1–8 of 56
CSDA · 2 publications · 60 citations
2
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2 publications · 656 citations
2
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1 publications · 17 citations
1
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ApJ · 1 publications · 51 citations
1
h-index
1 publications · 136 citations
1
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1–5