Approximate Bayesian Computation (ABC) in practice.

By Katalin Csilléry, Michael G. B. Blum, Oscar E. Gaggiotti, Olivier Francois. Trends in Ecology & Evolution, 2010

1,224 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76f20e477eb1423ee819046dc8d0c209ea5c61a5

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Citations in the corpus

Reconstructing routes of invasion using genetic data: why, how and so what? (2010, methodology): “General statistical features of ABC have been reviewed in two recent studies (Bertorelle et al. 2010; Csilléry et al. 2010).”
Computer simulations: tools for population and evolutionary genetics (2012, methodology): “Additionally, ABC provides a formal framework for model comparison using Bayes factors or deviance information criteria (DIC) 98,99 , which was not possible with pre-ABC approaches.”
Ecological genetics of invasive alien species (2011, methodology): “General statistical features of ABC have been reviewed in two recent papers (Bertorelle et al. 2010; Csillery et al. 2010) and some practical aspects that are important when using this method to make inferences about invasion routes can be found in Estoup and Guillemaud (2010).”
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, methodology): “The standard solution to these problems is offered by approximate Bayesian computation (ABC) methods [49, 9, 42, 51].”
Approximate Bayesian computational methods (2011, background): “We do not cover the increasingly wide array of applications of ABC here; see Csillèry et al. (2010a) for a survey of implementations of ABC in genomics and ecology.”
An introduction to sampling via measure transport (2016, background): “ we cannot evaluate the unnormalized target density and that we can access the target only through these samples. This is a common scenario in Bayesian inference problems with intractable likelihoods [92,52,23]. This sample-based setting is well suited to the computation of a triangular inverse tran”
IQ-TREE 3: phylogenomic inference software using complex evolutionary models (2026, neutral): “…such as evaluating new methods, testing hypotheses with parametric boot-strap (Giacomelli et al. 2025), performing approximate Bayesian computation (Csilléry et al. 2010), and generating training and testing data to help with the development of machine learning applications (Smith and Hahn 2023,…”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “, 2010), ecology (Csilléry et al., 2010) and computational biology (Friedman et al.”