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Approximate Bayesian computation (ABC) is an approach for sampling from an approximate posterior distribution in the presence of a computationally intractable likelihood function.
Distribution theory for tests based on the sample distribution function
Durbin, J. (1973) · 1973
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Statistical theory: The prequential approach
Dawid, A. P. (1984) · 1984
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On testing the validity of sequential probability forecasts
Seillier-Moiseiwitsch, F. and Dawid, A. P. (1993) · 1993
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Validation of software for Bayesian models using posterior quantiles
Cook, S. R., Gelman, A., and Rubin, D. B. (2006) · 2006
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Inference for stereological extremes
Bortot, P., Coles, S. G., and Sisson, S. A. (2007) · 2007
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Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F., and Raftery, A. E. (2007) · 2007
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Joint determination of topology, divergence time, and immigration in population trees
Beaumont, M. A. (2008) · 2008
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ABC likelihood-free methods for model choice in Gibbs random fields
Grelaud, A., Robert, C., Marin, J.-M., Rodolphe, F., and Taly, J. F. (2009) · 2009
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Model criticism based on likelihood-free inference, with an application to protein network evolution
Ratmann, O., Andrieu, C., Wiuf, C., and Richardson, S. (2009) · 2009
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Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood
Wegmann, D., Leuenberger, C., and Excoffier, L. (2009) · 2009
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Approximate Bayesian computation in evolution and ecology
Beaumont, M. A. (2010) · 2010
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ABC as a flexible framework to estimate demography over space and time: some cons, many pros
Bertorelle, G., Benazzo, A., and Mona, S. (2010) · 2010
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Choosing the summary statistics and the acceptance rate in approximate Bayesian computation
Blum, M. G. B. (2010b) · 2010
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Non-linear regression models for approximate Bayesian computation
Blum, M. G. B. and François, O. (2010) · 2010
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Approximate Bayesian computation (ABC) in practice
Csilléry, K., Blum, M. G. B., Gaggiotti, O. E., and François, O. (2010) · 2010
A novel approach for choosing summary statistics in approximate Bayesian computation
Aeschbacher, S., Beaumont, M. A., and Futschik, A. (2012) · 2012
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abc: An R package for approximate Bayesian computation (ABC)
Csilléry, K., François, O., and Blum, M. G. B. (2012) · 2012
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Approximate Bayesian computational methods
Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012) · 2012
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Simultaneous adjustment of bias and coverage probabilities for confidence intervals
Menéndez, P., Fan, Y., Garthwaite, P. H., and Sisson, S. A. (2012) · 2012
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Resequencing data provide no evidence for a human bottleneck in africa during the penultimate glacial period
Sjödin, P., Sjöstrand, A. E., Jakobsson, M., and Blum, M. G. B. (2012) · 2012
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ABCtoolbox: a versatile toolkit for approximate Bayesian computations
Wegmann, D., Leuenberger, C., Neuenschwander, S., and Excoffier, L. (2010) · 2010
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Likelihood-free Bayesian estimation of multivariate quantile distributions
Drovandi, C. C. and Pettitt, A. N. (2011) · 2011
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Likelihood-free Markov chain Monte Carlo
Sisson, S. A. and Fan, Y. (2011) · 2011
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Approximate Bayesian computation: A nonparametric perspective
Blum, M. G. B. (2010a)
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A comparative review of dimension reduction methods in approximate Bayesian computation
Blum, M. G. B., Nunes, M., Prangle, D., and Sisson, S. A. (2013) · 2013
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A new approach to choose acceptance cutoff for approximate Bayesian computation
Faisai, M., Futschick, A., and Hussain, I. (2013) · 2013
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Approximate Bayesian computation in population genetics
Beaumont, M. A., Zhang, W., and Balding, D. J. (2002) · 2035
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