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We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.
Bayesian inference using synthetic likelihood: asymptotics and adjustments, 2019
D. T. Frazier, D. J. Nott, C. Drovandi, and R. Kohn · 1902
Earlier work this paper cites.
An analysis of approximations for maximizing submodular set functions—i
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher · 1978
Earlier work this paper cites.
Curve fitting and optimal design for prediction
A. O’Hagan and J. F. C. Kingman · 1978
Earlier work this paper cites.
Bayes-Hermite quadrature
A. O’Hagan · 1991
Earlier work this paper cites.
Bayesian calibration of computer models
M. C. Kennedy and A. O’Hagan · 2001
Earlier work this paper cites.
Approximate Bayesian computation in population genetics
M. A. Beaumont, W. Zhang, and D. J. Balding · 2002
Earlier work this paper cites.
Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals
C. E. Rasmussen · 2003
Earlier work this paper cites.
Monte Carlo Statistical Methods
C. P. Robert and G. Casella · 2004
Earlier work this paper cites.
Dram: Efficient adaptive mcmc
H. Haario, M. Laine, A. Mira, and E. Saksman · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
Earlier work this paper cites.
The Bayesian Choice
C. P Robert · 2007
Earlier work this paper cites.
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A. Krause, A. Singh, and C. Guestrin · 2008
Earlier work this paper cites.
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B. Ankenman, B. L. Nelson, and J. Staum · 2010
Earlier work this paper cites.
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J. Azimi, F. Alan, and X. Z. Fern · 2010
Earlier work this paper cites.
E. Brochu, V. M. Cora, and N. de Freitas · 2010
Earlier work this paper cites.
Kriging Is Well-Suited to Parallelize Optimization , pages 131–162
D. Ginsbourger, R. Le Riche, and L. Carraro · 2010
Earlier work this paper cites.
Submodular dictionary selection for sparse representation
A. Krause and V. Cevher · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
N. Srinivas, A. Krause, S. Kakade, and M. Seeger · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
S. N. Wood · 2010
Earlier work this paper cites.
C. W. Yu and B. Clarke · 2011
Earlier work this paper cites.
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J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez · 2012
Earlier work this paper cites.
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J. Hakkarainen, A. Ilin, A. Solonen, M. Laine, H. Haario, J. Tamminen, E. Oja, and H. Järvinen · 2012
Earlier work this paper cites.
Entropy Search for Information-Efficient Global Optimization
P. Hennig and C. J. Schuler · 2012
Earlier work this paper cites.
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J. M. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder · 2012
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M. A. Osborne, D. Duvenaud, R. Garnett, C. E. Rasmussen, S. J. Roberts, and Z. Ghahramani · 2012
Cited alongside, same era.
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J. Snoek, H. Larochelle, and R. P. Adams · 2012
Cited alongside, same era.
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J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander · 2017
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