Approximate Bayesian computation in population genetics.

By MARK A. Beaumont, Wenyang Zhang, David J. Balding. 2002

3,037 citations recorded by the source corpus.

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

The frontier of simulation-based inference (2019, methodology): “Alternatively, a technique known as approximate Bayesian computation (ABC) (2, 3) compares the observed and simulated data based on some distance measure involving the summary statistics.”
Generalization in anti-causal learning (2018, methodology): “Simulation of an (approximately) known causal model also lies at the heart of ABC (Rubin et al., 1984), a method for likelihood-free Bayesian inference of the posterior that has proven potent in many areas, see (Beaumont et al., 2002; Toni et al., 2009; Beaumont, 2010).”
K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015, methodology): “Experiments on a simulated scenario and a real-world biological problem illustrate the effectiveness of the proposed algorithm.”
Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation (2017, methodology): “Also, Beaumont et al. (2002); Blum (2010); Blum and Fran¸cois (2010) have used modelling as a post-processing step to correct the approximation error of the nonzero threshold.”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, methodology): “2 Comparison of the posterior distributions on µ (left) and σ (right) when using an ABC algorithm 3 with distance ρ 1 (top) , a post-processed version by Beaumont et al.’s (2002) local regression (central) , and when using a standard Gibbs sampler (bottom) .”
Understanding the establishment success of non-indigenous fishes: lessons from population genetics (2007, background): “A promising future approach for analysing more complex invasion situations is approximate Bayesian computation (ABC; Beaumont et al., 2002).”
Likelihood-Free Inference of Population Structure and Local Adaptation in a Bayesian Hierarchical Model (2010, methodology): “A HIERARCHICAL APPROACH TO LIKELIHOODFREE INFERENCE The likelihood-free approach implemented in this study uses the regression-based method of conditional density estimation introduced in Beaumont et al. (2002).”
abc: an R package for approximate Bayesian computation (ABC) (2011, methodology): “The function abc implements three ABC algorithms for constructing the posterior distribution from the accepted θi’s: a rejection method, and regression-based correction methods that use either local linear regression [1] or neural networks [26].”
Generalized Fiducial Inference: A Review and New Results (2016, background): “Definition 1 illuminates the relationship between GFD and Approximate Bayesian Computations (ABC; Beaumont, Zhang, and Balding 2002).”
A simple approach to maximum intractable likelihood estimation (2013, methodology): “been proposed. For instance, Composite Likelihood methods (Cox and Reid, 2004), for approximating the likelihood function, and Approximate Bayesian Computational methods (ABC; Pritchard et al., 1999; Beaumont et al., 2002), for approximating the posterior distribution, have been extensively studied ”
Lack of confidence in approximate Bayesian computation model choice (2011, background): “We recall that ABC has first been introduced by population geneticists (2, 5) for statistical inference about the evolutionary history of species, because no likelihood-based approach existed apart from very simple and hence unrealistic situations.”
Adversarial Variational Optimization of Non-Differentiable Simulators (2017, methodology): “The prevalence and significance of this problem has motivated an active research effort in so-called likelihood-free inference algorithms such as Approximate Bayesian Computation (ABC) and density estimation-by-comparison algorithms (Beaumont et al., 2002; Marjoram et al., 2003; Sisson et al., 2007;”
Bayesian Synthetic Likelihood (2017, methodology): “(2022) formalize this superior scaling behaviour for basic ABC algorithms, and demonstrate that BSL performs similarly to ABC approaches with linear regression adjustment (Beaumont et al., 2002).”