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Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods.
(1945) On a method of estimating frequencies
Haldane, J · 1945
Earlier work this paper cites.
(1991) Bayes–Hermite quadrature
O’Hagan, A · 1991
Earlier work this paper cites.
(1992) Two notes on notation
Knuth, D. E · 1992
Earlier work this paper cites.
(1995) Bayes factors
Kass, R. E. & Raftery, A. E · 1995
Earlier work this paper cites.
(1998) Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., & Welch, W. J · 1998
Earlier work this paper cites.
(1999) An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K · 1999
Earlier work this paper cites.
(2020) Unbiased and efficient log-likelihood estimation with inverse binomial sampling
van Opheusden, B., Acerbi, L., & Ma, W. J · 2001
Earlier work this paper cites.
(2002) Bayesian Monte Carlo
Ghahramani, Z. & Rasmussen, C. E · 2002
Earlier work this paper cites.
(2003) Gaussian processes to speed up hybrid Monte Carlo for expensive Bayesian integrals
Rasmussen, C. E · 2003
Earlier work this paper cites.
(2003) Slice sampling
Neal, R. M · 2003
Earlier work this paper cites.
(2006) Dram: Efficient adaptive MCMC
Haario, H., Laine, M., Mira, A., & Saksman, E · 2006
Earlier work this paper cites.
(2007) Causal inference in multisensory perception
Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L · 2007
Earlier work this paper cites.
(2010) Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N · 2010
Earlier work this paper cites.
Brochu, E., Cora, V. M., & De Freitas, N · 2010
Earlier work this paper cites.
(2010) Stochastic kriging for simulation metamodeling
Ankenman, B., Nelson, B. L., & Staum, J · 2010
Earlier work this paper cites.
(2010) Visual fixations and the computation and comparison of value in simple choice
Krajbich, I., Armel, C., & Rangel, A · 2010
Earlier work this paper cites.
(2010) Temporal context calibrates interval timing
Jazayeri, M. & Shadlen, M. N · 2010
Earlier work this paper cites.
(2011) Lack of confidence in approximate Bayesian computation model choice
Robert, C. P., Cornuet, J.-M., Marin, J.-M., & Pillai, N. S · 2011
Earlier work this paper cites.
(2012) Active learning of model evidence using Bayesian quadrature
Osborne, M., Duvenaud, D. K., Garnett, R., Rasmussen, C. E., Roberts, S. J., & Ghahramani, Z · 2012
Earlier work this paper cites.
(2012) Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., & Adams, R. P · 2012
Cited alongside, same era.
(2012) Cases for the nugget in modeling computer experiments
Gramacy, R. B. & Lee, H. K · 2012
Cited alongside, same era.
(2012) Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing
Acerbi, L., Wolpert, D. M., & Vijayakumar, S · 2012
Cited alongside, same era.
(2013) Quantile-based optimization of noisy computer experiments with tunable precision
Picheny, V., Ginsbourger, D., Richet, Y., & Caplin, G · 2013
Cited alongside, same era.
(2013) Auto-encoding variational Bayes
Kingma, D. P. & Welling, M · 2013
Cited alongside, same era.
(2014) Sampling for inference in probabilistic models with fast Bayesian quadrature
Gunter, T., Osborne, M. A., Garnett, R., Hennig, P., & Roberts, S. J · 2014
(2018) Variational Bayesian Monte Carlo
Acerbi, L · 2018
Later among the works it cites.
(2018) Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Järvenpää, M., Gutmann, M. U., Vehtari, A., Marttinen, P., et al · 2018
Later among the works it cites.
(2018) Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Wang, H. & Li, J · 2018
Later among the works it cites.
(2018) Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation
Lyu, X., Binois, M., & Ludkovski, M · 2018
Later among the works it cites.
(2018) Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception
Acerbi, L., Dokka, K., Angelaki, D. E., & Ma, W. J · 2018
Later among the works it cites.
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Cited alongside, same era.
(2014) Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
Cited alongside, same era.
(2015) Bayesian active learning for posterior estimation
Kandasamy, K., Schneider, J., & Póczos, B · 2015
Cited alongside, same era.
(2015) Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees
Briol, F.-X., Oates, C., Girolami, M., & Osborne, M. A · 2015
Cited alongside, same era.
(2015) Origin and function of tuning diversity in macaque visual cortex
Goris, R. L., Simoncelli, E. P., & Movshon, J. A · 2015
Cited alongside, same era.
(2016) Bayesian optimization for likelihood-free inference of simulator-based statistical models
Gutmann, M. U. & Corander, J · 2016
Cited alongside, same era.
(2016) Fast
Papamakarios, G. & Murray, I · 2016
Cited alongside, same era.
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Akrami, A., Kopec, C. D., Diamond, M. E., & Brody, C. D · 2018
Later among the works it cites.
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Roy, N. A., Bak, J. H., Akrami, A., Brody, C., & Pillow, J. W · 2018
Later among the works it cites.
(2018) Yes, but did it work?: Evaluating variational inference
Yao, Y., Vehtari, A., Simpson, D., & Gelman, A · 2018
Later among the works it cites.
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Chen, I., Johansson, F. D., & Sontag, D · 2018
Later among the works it cites.
(2019) An exploration of acquisition and mean functions in Variational Bayesian Monte Carlo
Acerbi, L · 2019
Later among the works it cites.
(2019) Active multi-information source Bayesian quadrature
Gessner, A., Gonzalez, J., & Mahsereci, M · 2019
Later among the works it cites.
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Järvenpää, M., Gutmann, M. U., Pleska, A., Vehtari, A., Marttinen, P., et al · 2019
Later among the works it cites.
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Chai, H., Ton, J.-F., Garnett, R., & Osborne, M. A · 2019
Later among the works it cites.
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Letham, B., Karrer, B., Ottoni, G., Bakshy, E., et al · 2019
Later among the works it cites.
(2019) Automatic posterior transformation for likelihood-free inference
Greenberg, D. S., Nonnenmacher, M., & Macke, J. H · 2019
Later among the works it cites.
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Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., et al · 2019
Later among the works it cites.
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Kanagawa, M. & Hennig, P · 2019
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