ABC random forests for Bayesian parameter inference
暂无分享,去创建一个
Jean-Michel Marin | Christian P. Robert | Arnaud Estoup | Pierre Pudlo | Mathieu Ribatet | Louis Raynal
[1] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016, 1605.06376.
[2] David T. Frazier,et al. Asymptotic properties of approximate Bayesian computation , 2016, Biometrika.
[3] Arnaud Doucet,et al. An adaptive sequential Monte Carlo method for approximate Bayesian computation , 2011, Statistics and Computing.
[4] Jason M. Klusowski. Complete Analysis of a Random Forest Model , 2018, ArXiv.
[5] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[6] Carlo Gaetan,et al. Composite likelihood methods for space-time data , 2006 .
[7] Olivier François,et al. Non-linear regression models for Approximate Bayesian Computation , 2008, Stat. Comput..
[8] Kenny Q. Ye,et al. An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.
[9] Dennis Prangle,et al. Adapting the ABC distance function , 2015, 1507.00874.
[10] Nicolai Meinshausen,et al. Quantile Regression Forests , 2006, J. Mach. Learn. Res..
[11] Marcus W. Feldman,et al. The great human expansion , 2012, Resonance.
[12] M. Feldman,et al. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. , 1999, Molecular biology and evolution.
[13] J. Møller. Discussion on the paper by Feranhead and Prangle , 2012 .
[14] David Welch,et al. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems , 2009, Journal of The Royal Society Interface.
[15] Jean-Michel Marin,et al. Approximate Bayesian computational methods , 2011, Statistics and Computing.
[16] Yanan Fan,et al. Handbook of Approximate Bayesian Computation , 2018 .
[17] S. Sisson,et al. A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.
[18] C. Bustamante,et al. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. , 2013, American journal of human genetics.
[19] O. François,et al. Approximate Bayesian Computation (ABC) in practice. , 2010, Trends in ecology & evolution.
[20] Scott M. Williams,et al. The Great Migration and African-American Genomic Diversity , 2015, bioRxiv.
[21] Mark A. Beaumont,et al. Joint determination of topology, divergence time, and immigration in population trees , 2008 .
[22] Leonhard Held,et al. Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance , 2014, ArXiv.
[23] Arnaud Guyader,et al. New insights into Approximate Bayesian Computation , 2012, 1207.6461.
[24] Paul Fearnhead,et al. Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC , 2010, 1004.1112.
[25] Ryan D. Hernandez,et al. Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data , 2009, PLoS genetics.
[26] Matthew A. Nunes,et al. abctools: An R Package for Tuning Approximate Bayesian Computation Analyses , 2015, R J..
[27] Jean-Marie Cornuet,et al. ABC model choice via random forests , 2014, 1406.6288.
[28] John A. Rogersa,et al. Correction for ‘ ‘ Sequential Monte Carlo without likelihoods , 2009 .
[29] Jean-Marie Cornuet,et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data , 2014, Bioinform..
[30] Richard Durbin,et al. Inferring human population size and separation history from multiple genome sequences , 2014 .
[31] M. Beaumont,et al. CodABC: A Computational Framework to Coestimate Recombination, Substitution, and Molecular Adaptation Rates by Approximate Bayesian Computation , 2015, Molecular biology and evolution.
[32] Jan Hasenauer,et al. pyABC: distributed, likelihood-free inference , 2017, bioRxiv.
[33] Michael J. Hickerson,et al. Detecting Concerted Demographic Response across Community Assemblages Using Hierarchical Approximate Bayesian Computation , 2014, Molecular biology and evolution.
[34] Michael Lachmann,et al. Inferring the history of population size change from genome-wide SNP data. , 2012, Molecular biology and evolution.
[35] N. Reid,et al. AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS , 2011 .
[36] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[37] M. Beaumont. Approximate Bayesian Computation in Evolution and Ecology , 2010 .
[38] Jean-Michel Marin,et al. Bayesian Essentials with R , 2013 .
[39] Jean-Michel Marin,et al. Likelihood-Free Model Choice , 2015, Handbook of Approximate Bayesian Computation.
[40] L. Excoffier,et al. Robust Demographic Inference from Genomic and SNP Data , 2013, PLoS genetics.
[41] Richard R. Hudson,et al. Generating samples under a Wright-Fisher neutral model of genetic variation , 2002, Bioinform..
[42] David Reich,et al. The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States , 2015, American journal of human genetics.
[43] Paul Marjoram,et al. Statistical Applications in Genetics and Molecular Biology Approximately Sufficient Statistics and Bayesian Computation , 2011 .
[44] Paul Fearnhead,et al. On the Asymptotic Efficiency of ABC Estimators , 2015 .
[45] P. Donnelly,et al. Inferring coalescence times from DNA sequence data. , 1997, Genetics.
[46] C. Bishop. Mixture density networks , 1994 .
[47] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[48] Andreas Ziegler,et al. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.
[49] Jan Hasenauer,et al. A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo , 2017, CMSB.
[50] Jean-Marie Cornuet,et al. Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation , 2008, Bioinform..
[51] Saso Dzeroski,et al. Ensembles of Multi-Objective Decision Trees , 2007, ECML.
[52] Paul Marjoram,et al. Choice of Summary Statistic Weights in Approximate Bayesian Computation , 2011, Statistical applications in genetics and molecular biology.
[53] Katalin Csill'ery,et al. abc: an R package for approximate Bayesian computation (ABC) , 2011, 1106.2793.
[54] D. Balding,et al. Statistical Applications in Genetics and Molecular Biology On Optimal Selection of Summary Statistics for Approximate Bayesian Computation , 2011 .
[55] Olivier Gascuel,et al. Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study , 2017, PLoS Comput. Biol..