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There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs.
Approximate Bayesian computation in population genetics
M. A. Beaumont, W. Zhang, and D. J. Balding · 2002
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Markov chain Monte Carlo
W. R. Gilks · 2005
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Church: a language for generative models
N. D. Goodman, V. K. Mansinghka, D. Roy, K. Bonawitz, and J. B. Tenenbaum · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
C. Andrieu and G. O. Roberts · 2009
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Figaro: An object-oriented probabilistic programming language
A. Pfeffer · 2009
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Approximate Bayesian computation (ABC) in practice
K. Csilléry, M. G. Blum, O. E. Gaggiotti, and O. François · 2010
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Venture: a higher-order probabilistic programming platform with programmable inference
V. Mansinghka, D. Selsam, and Y. Perov · 2014
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A compilation target for probabilistic programming languages
B. Paige and F. Wood · 2014
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A new approach to probabilistic programming inference
F. Wood, J. W. van de Meent, and V. Mansinghka · 2014
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Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
C. Andrieu, M. Vihola, et al · 2015
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The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
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Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
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Convergence of Sequential Monte Carlo-based Sampling Methods
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On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
A.-M. Lyne, M. Girolami, Y. Atchade, H. Strathmann, D. Simpson, et al · 2015
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Pseudo-marginal Hamiltonian Monte Carlo
F. Lindsten and A. Doucet · 2016
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Practical optimal experiment design with probabilistic programs
L. Ouyang, M. H. Tessler, D. Ly, and N. Goodman · 2016
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Probabilistic integration: A role for statisticians in numerical analysis?
F.-X. Briol, C. J. Oates, M. Girolami, M. A. Osborne, and D. Sejdinovic · 2015
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Bayesian Optimization for Probabilistic Programs
T. Rainforth, T. A. Le, J.-W. van de Meent, M. A. Osborne, and F. Wood
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Interacting particle Markov chain Monte Carlo
T. Rainforth, C. A. Naesseth, F. Lindsten, B. Paige, J.-W. van de Meent, A. Doucet, and F. Wood
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