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This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018).
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Annealed importance sampling
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Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
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Statistical inference for noisy nonlinear ecological dynamic systems
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Likelihood-free MCMC
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Likelihood-free Bayesian methods for inference using stochastic evolutionary models of Mycobacterium tuberculosis
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Generalized multiple-point Metropolis algorithms for approximate Bayesian computation
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Hamiltonian ABC
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Parameter estimation in hidden Markov models with intractable likelihoods using sequential Monte Carlo
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A likelihood-free reverse sampler of the posterior distribution
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Efficient likelihood-free Bayesian computation for household epidemics
Neal, P. (2012) · 2012
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Nott, D. J., L. Marshall, and M. N. Tran (2012) · 2012
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On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
Peters, G. W., Y. Fan, and S. A. Sisson (2012) · 2012
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Likelihood-free parallel tempering
Baragatti, M., A. Grimaud, and D. Pommeret (2013) · 2013
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Lazy ABC
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A bootstrap likelihood approach to Bayesian computation
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Inference in generative models using the Wasserstein distance
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The use of a single pseudo-sample in approximate Bayesian computation
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The validation of approximate Bayesian computation: Theory and practice
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Adapting the ABC distance function
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A rare event approach to high-dimensional approximate Bayesian computation
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New methods for infinite and high-dimensional approximate Bayesian computation
Rodrigues, G. S. (2017) · 2017
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Multilevel rejection sampling for approximate Bayesian computation
Warne, D. J., R. E. Baker, and M. J. Simpson (2017) · 2017
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Theoretical and methodological aspects of MCMC computations with noisy likelihoods
Andrieu, C., A. Lee, and M. Vihola (2018) · 2018
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ABC and indirect inference
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