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Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model.
The structure of cold dark matter halos
Navarro, J. F. (1996) · 1996
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The structure of cold dark matter halos
Navarro, J. F. (1996) · 1996
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A wavelet tour of signal processing
Mallat, S. (1999) · 1999
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A wavelet tour of signal processing
Mallat, S. (1999) · 1999
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Statistical modeling: The two cultures
Breiman, L. (2001) · 2001
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The elements of statistical learning: data mining, inference, and prediction
Hastie, T., R. Tibshirani, and J. H. Friedman (2001) · 2001
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Statistical modeling: The two cultures
Breiman, L. (2001) · 2001
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The elements of statistical learning: data mining, inference, and prediction
Hastie, T., R. Tibshirani, and J. H. Friedman (2001) · 2001
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Tailor-made tests for goodness of fit to semiparametric hypotheses
Bickel, P. J., Y. Ritov, and T. M. Stoker (2006) · 2006
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Nonparametric functional data analysis: theory and practice
Ferraty, F. and P. Vieu (2006) · 2006
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Tailor-made tests for goodness of fit to semiparametric hypotheses
Bickel, P. J., Y. Ritov, and T. M. Stoker (2006) · 2006
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Nonparametric functional data analysis: theory and practice
Ferraty, F. and P. Vieu (2006) · 2006
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Sequential Monte Carlo without likelihoods
Sisson, S. A., Y. Fan, and M. M. Tanaka (2007) · 2007
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Sequential Monte Carlo without likelihoods
Sisson, S. A., Y. Fan, and M. M. Tanaka (2007) · 2007
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Weak gravitational lensing and its cosmological applications
Hoekstra, H. and B. Jain (2008) · 2008
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Cosmology with weak lensing surveys
Munshi, D., P. Valageas, L. Van Waerbeke, and A. Heavens (2008) · 2008
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Weak gravitational lensing and its cosmological applications
Hoekstra, H. and B. Jain (2008) · 2008
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Cosmology with weak lensing surveys
Munshi, D., P. Valageas, L. Van Waerbeke, and A. Heavens (2008) · 2008
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Adaptive approximate bayesian computation
Beaumont, M. A., J. Cornuet, J. Marin, and C. P. Robert (2009) · 2009
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Sparse additive models
Ravikumar, P., J. Lafferty, H. Liu, and L. Wasserman (2009) · 2009
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Adaptive approximate bayesian computation
Beaumont, M. A., J. Cornuet, J. Marin, and C. P. Robert (2009) · 2009
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Sparse additive models
Ravikumar, P., J. Lafferty, H. Liu, and L. Wasserman (2009) · 2009
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Approximate bayesian computation in evolution and ecology
Beaumont, M. A. (2010) · 2010
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Approximate bayesian computation: a nonparametric perspective
Blum, M. G. (2010) · 2010
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Non-linear regression models for approximate bayesian computation
Blum, M. G. B. and O. François (2010) · 2010
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Approximate bayesian computation in evolution and ecology
Beaumont, M. A. (2010) · 2010
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Approximate bayesian computation: a nonparametric perspective
Blum, M. G. (2010) · 2010
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Non-linear regression models for approximate bayesian computation
Blum, M. G. B. and O. François (2010) · 2010
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Simulations of Wide-field Weak-lensing Surveys. II. Covariance Matrix of Real-space Correlation Functions
Sato, M., M. Takada, T. Hamana, and T. Matsubara (2011, June) · 2011
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Simulations of Wide-field Weak-lensing Surveys. II. Covariance Matrix of Real-space Correlation Functions
Sato, M., M. Takada, T. Hamana, and T. Matsubara (2011, June) · 2011
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A novel approach for choosing summary statistics in approximate Bayesian c omputation
Aeschbacher, S., M. A. Beaumont, and A. Futschik (2012) · 2012
Earlier work this paper cites.
Approximate bayesian computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift
Cameron, E. and A. Pettitt (2012) · 2012
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abc: an r package for approximate bayesian computation (abc)
Csillery, K., O. Francois, and M. G. B. Blum (2012) · 2012
Cited alongside, same era.
Estimation of demo-genetic model probabilities with approximate bayesian computation using linear discriminant analysis on summary statistics
Estoup, A., E. Lombaert, J. Marin, et al. (2012) · 2012
Cited alongside, same era.
Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Fearnhead, P. and D. Prangle (2012) · 2012
Cited alongside, same era.
Approximate Bayesian computational methods
Marin, J. M., P. Pudlo, C. P. Robert, and R. J. Ryder (2012) · 2012
Cited alongside, same era.
A novel approach for choosing summary statistics in approximate Bayesian c omputation
Aeschbacher, S., M. A. Beaumont, and A. Futschik (2012) · 2012
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T. and C. Guestrin (2016) · 2016
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On selection of statistics for approximate bayesian computing (or the method of simulated moments)
Creel, M. and D. Kristensen (2016) · 2016
Later among the works it cites.
Choosing summary statistics by least angle regression for approximate bayesian computation
Faisal, M., A. Futschik, I. Hussain, and M. Abd-el. Moemen (2016) · 2016
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A spectral series approach to high-dimensional nonparametric regression
Lee, A. B. and R. Izbicki (2016) · 2016
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Mocking the weak lensing universe: The lenstools python computing package
Petri, A. (2016) · 2016
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Cosmology from cosmic shear with Dark Energy Survey Science Verification data
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Approximate bayesian computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift
Cameron, E. and A. Pettitt (2012) · 2012
Cited alongside, same era.
abc: an r package for approximate bayesian computation (abc)
Csillery, K., O. Francois, and M. G. B. Blum (2012) · 2012
Cited alongside, same era.
Estimation of demo-genetic model probabilities with approximate bayesian computation using linear discriminant analysis on summary statistics
Estoup, A., E. Lombaert, J. Marin, et al. (2012) · 2012
Cited alongside, same era.
Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Fearnhead, P. and D. Prangle (2012) · 2012
Cited alongside, same era.
Approximate Bayesian computational methods
Marin, J. M., P. Pudlo, C. P. Robert, and R. J. Ryder (2012) · 2012
Cited alongside, same era.
A comparative review of dimension reduction methods in approximate bayesian computation
Blum, M. G. B., M. A. Nunes, D. Prangle, and S. A. Sisson (2013) · 2013
Cited alongside, same era.
Approximate bayesian computation via regression density estimation
Fan, Y., D. J. Nott, and S. A. Sisson (2013) · 2013
Cited alongside, same era.
Abbott, T., F. B. Abdalla, S. Allam, et al. (2016, July) · 2016
Later among the works it cites.
Xgboost: A scalable tree boosting system
Chen, T. and C. Guestrin (2016) · 2016
Later among the works it cites.
On selection of statistics for approximate bayesian computing (or the method of simulated moments)
Creel, M. and D. Kristensen (2016) · 2016
Later among the works it cites.
Choosing summary statistics by least angle regression for approximate bayesian computation
Faisal, M., A. Futschik, I. Hussain, and M. Abd-el. Moemen (2016) · 2016
Later among the works it cites.
A spectral series approach to high-dimensional nonparametric regression
Lee, A. B. and R. Izbicki (2016) · 2016
Later among the works it cites.
Mocking the weak lensing universe: The lenstools python computing package
Petri, A. (2016) · 2016
Later among the works it cites.
KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing
Hildebrandt, H., M. Viola, C. Heymans, et al. (2017, February) · 2017
Later among the works it cites.
Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R. and A. Lee (2017) · 2017
Later among the works it cites.
Convergence of regression adjusted approximate bayesian computation
Li, W. and P. Fearnhead (2017) · 2017
Later among the works it cites.
Flexible statistical inference for mechanistic models of neural dynamics
Lueckmann, J. M., P. J. Goncalves, G. Bassetto, et al. (2017) · 2017
Later among the works it cites.
Weak lensing for precision cosmology
Mandelbaum, R. (2017) · 2017
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ABC random forests for Bayesian parameter inference
Raynal, L., J.-M. Marin, , et al. (2017) · 2017
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Dynamical models for the Sculptor dwarf spheroidal in a
Strigari, L. E., C. S. Frenk, and S. D. M. White (2017) · 2017
Later among the works it cites.
KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing
Hildebrandt, H., M. Viola, C. Heymans, et al. (2017, February) · 2017
Later among the works it cites.
Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R. and A. Lee (2017) · 2017
Later among the works it cites.
Convergence of regression adjusted approximate bayesian computation
Li, W. and P. Fearnhead (2017) · 2017
Later among the works it cites.
Flexible statistical inference for mechanistic models of neural dynamics
Lueckmann, J. M., P. J. Goncalves, G. Bassetto, et al. (2017) · 2017
Later among the works it cites.
Weak lensing for precision cosmology
Mandelbaum, R. (2017) · 2017
Later among the works it cites.
ABC random forests for Bayesian parameter inference
Raynal, L., J.-M. Marin, , et al. (2017) · 2017
Later among the works it cites.
Dynamical models for the Sculptor dwarf spheroidal in a
Strigari, L. E., C. S. Frenk, and S. D. M. White (2017) · 2017
Later among the works it cites.
In preparation
Liu, T. and M. Walker (2018) · 2018
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RFCDE: Random forests for conditional density estimation
Pospisil, T. and A. B. Lee (2018) · 2018
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In preparation
Liu, T. and M. Walker (2018) · 2018
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RFCDE: Random forests for conditional density estimation
Pospisil, T. and A. B. Lee (2018) · 2018
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Approximate bayesian computation in population genetics
Beaumont, M. A., W. Zhang, and D. J. Balding (2002) · 2035
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Approximate bayesian computation in population genetics
Beaumont, M. A., W. Zhang, and D. J. Balding (2002) · 2035
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