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Approximate Bayesian computation (ABC) is a widely used inference method in Bayesian statistics to bypass the point-wise computation of the likelihood.
Approximate Bayesian computation in population genetics
M. A Beaumont, W. Zhang, and D. J Balding · 2002
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Sequential Monte Carlo samplers
P. Del Moral, A. Doucet, and A. Jasra · 2006
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Gaussian processes for Machine Learning
C. Rasmussen and C. Williams · 2006
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From ϵ \epsilon -entropy to kl-entropy: Analysis of minimum information complexity density estimation
Tong Zhang et al · 2006
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PAC-Bayesian Supervised Classification , volume 56
O. Catoni · 2007
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Numerical recipes 3rd edition: The art of scientific computing
William H Press · 2007
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Bayesian inference with rescaled Gaussian process priors
Aad van der Vaart, Harry van Zanten, et al · 2007
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Reproducing kernel Hilbert spaces of Gaussian priors
Aad W van der Vaart, J Harry van Zanten, et al · 2008
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Abc likelihood-free methods for model choice in gibbs random fields
A. Grelaud, C. P Robert, J. Marin, F. Rodolphe, and J. Taly · 2009
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Particle Markov Chain Monte Carlo
C. Andrieu, A. Doucet, and R. Holenstein · 2010
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Deviation inequalities for sums of weakly dependent time series
Wintenberger Olivier et al · 2010
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Safe learning: bridging the gap between bayes, mdl and statistical learning theory via empirical convexity
P. Grünwald · 2011
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Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
A. Jasra, D. A Stephens, A. Doucet, and T. Tsagaris · 2011
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Approximate Bayesian Computation for astronomical model analysis: a case study in galaxy demographics and morphological transformation at high redshift
E. Cameron and AN Pettitt · 2012
Cited alongside, same era.
An adaptive sequential Monte Carlo method for approximate Bayesian computation
P. Del Moral, A. Doucet, and A. Jasra · 2012
Cited alongside, same era.
Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC
P. Fearnhead and D. Prangle · 2012
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Learning without concentration
Shahar Mendelson · 2014
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The rate of convergence for approximate Bayesian computation
S. Barber, J. Voss, and M. Webster · 2015
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Towards automatic calibration of the number of state particles within the SMC 2 algorithm
N. Chopin, J. Ridgway, M. Gerber, and O. Papaspiliopoulos · 2015
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On the Asymptotic Efficiency of ABC Estimators
W. Li and P. Fearnhead · 2015
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Computation of Gaussian orthant probabilities in high dimension
J Ridgway · 2015
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On the properties of variational approximations of Gibbs posterior
P. Alquier, J. R., and N. Chopin · 2016
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Concentration inequalities: A nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
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SMC 2 : an efficient algorithm for sequential analysis of state space models
N. Chopin, O. Papaspiliopoulos, and P. E. Jacob · 2013
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Approximate bayesian image interpretation using generative probabilistic graphics programs
Vikash Mansinghka, Tejas D Kulkarni, Yura N Perov, and Josh Tenenbaum · 2013
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Inconsistency of bayesian inference for misspecified linear models, and a proposal for repairing it
P. Grünwald and T. van Ommen · 2014
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Relevant statistics for bayesian model choice
J. Marin, N. S Pillai, C. P Robert, and J. Rousseau · 2014
Cited alongside, same era.
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Asymptotic properties of approximate bayesian computation
D. T Frazier, G. M Martin, C. P Robert, and J. Rousseau · 2016
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Fast rates with unbounded losses
P. Grünwald and N. Mehta · 2016
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Concentration of tempered posterior and their variational approximations
P. Alquier and J Ridgway · 2017
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Inference in generative models using the Wasserstein distance
E. Bernton, P. E Jacob, M. Gerber, and C. P Robert · 2017
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Bayesian fractional posteriors
A. Bhattacharya, D. Pati, and Y. Yang · 2017
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