Posterior Approximation is a computational method in Bayesian statistics that belongs to the class of inference techniques designed to estimate probability distributions, specifically distinguished by its aim to approximate the posterior distribution of model parameters when exact inference is intractable, often employed within frameworks such as Approximate Bayesian Computation or Variational Inference.
569
Publications
46.5K
Citations
1K
Authors
445
Institutions
Publications per year
2017–2026
153
Publications
569
Approximate Bayesian Computation in Population Genetics
Mark Beaumont, Wenyang Zhang, David J. Balding · Genetics · 2002 · 3.1K citations
1–5 of 569
Authors
1K
- CP
12
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- TB
10
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- MI
10
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- DB
9
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- AF
8
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1–5 of 1K
Institutions
445
University of California, Berkeley
Berkeley, United States
15 h-index
33 publications
6.6K citations
Stanford University
Stanford, United States
15 h-index
24 publications
2.1K citations
Centre National de la Recherche Scientifique
Paris, France
13 h-index
25 publications
2.9K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
18 publications
8.4K citations
University College London
London, United Kingdom
11 h-index
16 publications
1.1K citations
Imperial College London
London, United Kingdom
11 h-index
20 publications
5.3K citations
National University of Singapore
Singapore, Singapore
11 h-index
17 publications
1.1K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
28 publications
888 citations
1–8 of 445
Venues
- arXiv (Cornell University)
Repository · 80 publications · 5K citations
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- Journal of the American Statistical Association
33 publications · 11.7K citations
24
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- Statistics and Computing
18 publications · 2.4K citations
18
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- Biometrika
14 publications · 1.2K citations
14
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- Journal of the Royal Statistical Society Series B (Statistical Methodology)
14 publications · 3K citations
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