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Approximate Bayesian computation (ABC) methods can be used to sample from posterior distributions when the likelihood function is unavailable or intractable, as is often the case in biological systems.
On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
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Stock and recruitment
William E Ricker · 1954
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The calculation of posterior distributions by data augmentation: Comment: A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: The sir algorithm
Donald B Rubin · 1987
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The likelihood principle
James O Berger and Robert L Wolpert · 1988
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Markov chain Monte Carlo in practice
Walter R Gilks, Sylvia Richardson, and David Spiegelhalter · 1995
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Population growth of human y chromosomes: a study of y chromosome microsatellites
Jonathan K Pritchard, Mark T Seielstad, Anna Perez-Lezaun, and Marcus W Feldman · 1999
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An introduction to sequential monte carlo methods
Arnaud Doucet, Nando De Freitas, and Neil Gordon · 2001
Cited alongside, same era.
The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
Cited alongside, same era.
Approximate bayesian computation in population genetics
Mark A Beaumont, Wenyang Zhang, and David J Balding · 2002
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Population monte carlo
Olivier Cappé, Arnaud Guillin, Jean-Michel Marin, and Christian P Robert · 2004
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Adaptive approximate bayesian computation
Mark A Beaumont, Jean-Marie Cornuet, Jean-Michel Marin, and Christian P Robert · 2009
Cited alongside, same era.
Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems
Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, and Michael PH Stumpf · 2009
Cited alongside, same era.
Approximate bayesian computation in evolution and ecology
Mark A Beaumont · 2010
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
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Density ratio estimation in machine learning
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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On optimality of kernels for approximate bayesian computation using sequential monte carlo
Sarah Filippi, Chris P Barnes, Julien Cornebise, and Michael PH Stumpf · 2013
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Likelihood-free inference by ratio estimation
Ritabrata Dutta, Jukka Corander, Samuel Kaski, and Michael U Gutmann · 2016
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Antti Kangasrääsiö, Jarno Lintusaari, Kusti Skytén, Marko Järvenpää, Henri Vuollekoski, Michael Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski, et al · 2016
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