Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

By Daniel Wegmann, Christoph Leuenberger, Laurent Excoffier. Genetics, 2009

397 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2a513e9b2578c5545ac0f39825b7f2e3a8d76d60

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Citations in the corpus

A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation (2012, background): “2003 , Wegmann 94 et al .”
Statistical inference for stochastic simulation models--theory and application. (2011, background): “Wegmann et al. (2009) extract the most important components of a larger set of summary statistics by partial least square transformations.”
Likelihood-free inference via classification (2014, methodology): “In previous work, regression with the parameters θ as response variables was used to generate summary statistics from a larger pool of candidates (Aeschbacher et al. 2012; Fearnhead and Prangle 2012; Wegmann et al. 2009).”
A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “A number of methods, primarily based on dimension reduction ideas, have been proposed to achieve this (Joyce and Marjoram 2008; Wegmann et al. 2009; Nunes and Balding 2010; Blum and François 2010; Blum 2010b; Fearnhead and Prangle 2012).”
Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019, background): “Several procedures have been proposed to improve the acceptance rate by guiding simulations based on previously accepted states (Toni et al., 2008; Marjoram et al., 2003; Wegmann et al., 2009).”
Kernel approximate Bayesian computation in population genetic inferences (2012, methodology): “Recent progress in ABC algorithm development also has involved the use of Markov chain Monte Carlo or sequential Monte Carlo methods (Marjoram et al., 2003; Sisson et al., 2007; Beaumont et al., 2009; Wegmann et al., 2009; Drovandi and Pettit, 2011).”
On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators (2015, background): “Finally, a number of papers have looked at the choice of summary statistics (e.g. Wegmann et al., 2009; Blum, 2010; Prangle et al., 2014).”
Likelihood-Free Inference of Population Structure and Local Adaptation in a Bayesian Hierarchical Model (2010, methodology): “Transformation of symmetric summary statistics: Previous studies have suggested the use in ABC of transformations, including rotations of the summary statistics (Fagundes et al. 2007; Wegmann et al. 2009).”
Approximate Bayesian Computation in Evolution and Ecology (2010, methodology): “More recently, tests of ABC methods under repeated sampling from the prior have been performed (Rosenblum et al. 2007, Wegmann et al. 2009), and these more accurately diagnose problems.”
Robust Demographic Inference from Genomic and SNP Data (2013, background): “The age of the expansion is slightly older than the divergence time between two Western Niger-Congo populations estimated previously (140 generations, [13]), and intermediate between the age of the Niger-Congo languages ( , 10 Kya, [56]), and that of the Bantu expansion ( , 5 Kya, [57]).”
Reconstructing routes of invasion using genetic data: why, how and so what? (2010, methodology): “Several ABC programs have recently been developed to provide nonspecialist users with solutions varying in the extent to which they are user-friendly (Cornuet et al. 2008; Foll et al. 2008; Hickerson & Meyer 2008; Jobin & Mountain 2008; Tallmon et al. 2008; Lopes et al. 2009; Weg- mann et al. 2009).”
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation (2008, methodology): “Consequently, a line of research including the works of Tavaré et al. (1997), Weiss and von Haeseler (1998), Pritchard et al. (1999) and Marjoram et al. (2003) developed a new approach termed Approximate Bayesian Computation (or ABC) by Beaumont et al. (2002).”
Likelihood-free Markov chain Monte Carlo (2010, methodology): “Wegmann et al. (2009) implement the LF-MCMC algorithm with a large value to enhance chain mixing, and then perform a regressionbased adjustment (Beaumont et al., 2002; Blum and Francois, 2009) to improve the final posterior approximation.”
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models (2015, background): “One difficulty is the assessment of the discrepancy between the observed and simulated data (Joyce and Marjoram, 2008; Wegmann et al., 2009; Nunes and Balding, 2010; Fearnhead and Prangle, 2012; Aeschbacher et al., 2012; Gutmann et al., 2014).”
ABCtoolbox: a versatile toolkit for approximate Bayesian computations (2010, methodology): “Based on such a test dataset, ABCestimator computes accuracy measures, such as bias, mean squared errors and coverage properties [9].”