Approximate Bayesian Computation in Evolution and Ecology

By J. Bronstein, R. Plevin. 2011

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Citations in the corpus

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).”
Normalizing Flows for Probabilistic Modeling and Inference (2019, background): “, 2020), or approximate Bayesian computation (Beaumont et al., 2002; Beaumont, 2010).”
Deep and Hierarchical Implicit Models (2017, background): “Such models pervade physical systems in ﬁelds such as population genetics, statistical physics, and ecology (Pritchard et al., 1999; Anelli et al., 2008; Beaumont, 2010).”
On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators (2015, methodology): “Arguably the first approximate Bayesian computation method was that of Pritchard et al. (1999), and these methods have been popular within population genetics (Beaumont et al., 2002), ecology (Beaumont, 2010) and systems biology (Toni et al., 2009).”
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018, unknown): “Approximate Bayesian Computation [4, 5, 8, 42, 46].”
The Rate of Convergence for Approximate Bayesian Computation (2013, background): “…(Drovandi and Pettitt, 2011), genetic evolution (Thornton and Andolfatto, 2006; Fagundes et al., 2007; Ratmann et al., 2009; Wegmann and Excoﬃer, 2010; Beaumont, 2010; Wilkinson et al., 2011), population migration (Guillemaud et al., 2010) and conservation studies (Lopes and Boessenkool, 2010).”
Approximate Bayesian computation via regression density estimation (2012, methodology): “3 Connections with other methods Bonassi et al. (2011) provide a method to directly estimate the posterior density.”
Likelihood-Free Inference by Ratio Estimation (2016, methodology): “For a comprehensive introduction to the ﬁeld, we refer the reader to the review papers by Beaumont (2010); Hartig et al. (2011); Marin et al. (2012); Lintusaari et al. (2017); Sisson et al. (2018).”
Challenges in linking trait patterns to niche differentiation (2016, methodology): “For example, approximate Bayesian computation algorithms (Beaumont 2010) have been used to estimate the relative importance of different community assembly processes in shaping trait patterns by comparing trait-based summary statistics produced under different model assumptions about process with…”
Likelihood-free inference via classification (2014, background): “Their scope of applicability is extremely wide ranging from genetics and ecology (Beaumont 2010) to economics (Gouriéroux et al.”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “We have concentrated our discussion mainly on parameter estimation, but once appropriate likelihood approximations are established, model selection and uncertainty estimation can, in principle, be done in the same way as in other statistical applications (e.g. Beaumont 2010; Toni & Stumpf 2010).”
Hierarchical Implicit Models and Likelihood-Free Variational Inference (2017, methodology): “ABC steps around the intractable likelihood by applying summary statistics to measure the closeness of simulated samples to real observations.”
Introgression of crop alleles into wild or weedy populations (2013, unknown): “12:201–6 Beaumont MA. 2010.”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, background): “It is also the area that concentrates most of the criticisms addressed against ABC: while some are easily dismissed (see, e.g., Templeton 2008, 2010; Beaumont et al. 2010; Berger et al. 2010), the impact of the choice of the summary statistics on the value of the posterior probability remains a…”
ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation (2011, background): “Examples can be found across a wide range of elds, from biology (Beaumont, 2010) to economics (Gouriéroux et al., 1993), and the underlying model does not need to be complex - indeed, some of the simplest queuing models are intractable in that way (Blum & François, 2010).”
MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity (2011, methodology): “For further information about the bourgeoning set of methods and strategies being developed for ABC, [12-14] provide thorough overviews.”
Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2013, methodology): “Sophisticated approaches derived from Monte Carlo filtering are being developed and applied to obtain best-fit estimates of parameters whose values cannot be otherwise established (Piou, Berger & Grimm 2009; Beaumont 2010; Csillery et al. 2010).”