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Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences.
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Foster, A., Jankowiak, M., O’Meara, M., Whye Teh, Y., and Rainforth, T · 1911
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Asymptotic evaluation of certain markov process expectations for large time. iv
Donsker, M. D. and Varadhan, S. R. S · 1983
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Simulation-based optimal design
Müller, P · 1999
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Probability and random processes , volume 80
Grimmett, G. and Stirzaker, D · 2001
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Geant4-a simulation toolkit
Agostinelli, S., Allison, J., Amako, K., Apostolakis, J., M Araujo, H., Arce, P., Asai, M., A Axen, D., Banerjee, S., Barrand, G., Behner, F., Bellagamba, L., Boudreau, J., Broglia, L., Brunengo, A., Chauvie, S., Chuma, J., Chytracek, R., Cooperman, G., and Zschiesche, D · 2003
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Rasmussen, C. E. and Williams, C. K. I · 2005
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Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
Spall, J · 2005
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I · 2010
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
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Likelihood-free inference in cosmology: Potential for the estimation of luminosity functions
M. Schafer, C. and Freeman, P · 2012
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A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models
Donnet, S. and Samson, A · 2013
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Bayesian experimental design for models with intractable likelihoods
Drovandi, C. C. and Pettitt, A. N · 2013
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Towards Bayesian experimental design for nonlinear models that require a large number of sampling times
Ryan, E., Drovandi, C. C., Thompson, H., and Pettitt, A. N · 2014
Cited alongside, same era.
On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics
Price, D. J., Bean, N. G., Ross, J. V., and Tuke, J · 2015
Cited alongside, same era.
Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation
Vo, B. N., Drovandi, C. C., Pettitt, A. N., and Simpson, M. J · 2015
Cited alongside, same era.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
Gutmann, M. U. and Corander, J · 2016
Cited alongside, same era.
Likelihood-free extensions for Bayesian sequentially designed experiments
Hainy, M., Drovandi, C. C., and McGree, J · 2016
Cited alongside, same era.
Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D · 2018
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Likelihood-free inference in high dimensions with synthetic likelihood
Ong, V., Nott, D., Tran, M.-N., Sisson, S., and Drovandi, C · 2018
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Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation
Overstall, A. M. and McGree, J. M · 2018
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An induced natural selection heuristic for finding optimal Bayesian experimental designs
Price, D. J., Bean, N. G., Ross, J. V., and Tuke, J · 2018
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Handbook of Approximate Bayesian Computation. , chapter Overview of Approximate Bayesian Computation
Sisson, S., Fan, Y., and Beaumont, M · 2018
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F-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Fast epsilon-free inference of simulation models with Bayesian conditional density estimation
Papamakarios, G. and Murray, I · 2016
Cited alongside, same era.
A review of modern computational algorithms for Bayesian optimal design
Ryan, E. G., Drovandi, C. C., McGree, J. M., and Pettitt, A. N · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2016
Cited alongside, same era.
Likelihood-free inference by ratio estimation
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U · 2016
Cited alongside, same era.
Frequency-dependent selection in vaccine-associated pneumococcal population dynamics
Corander, J., Fraser, C., Gutmann, M., Arnold, B., Hanage, W., Bentley, S., Lipsitch, M., and Croucher, N · 2017
Cited alongside, same era.
Fundamentals and recent developments in approximate Bayesian computation
Lintusaari, J., Gutmann, M., Dutta, R., Kaski, S., and Corander, J · 2017
Cited alongside, same era.
Asenov, M., Rutkauskas, M., Reid, D., Subr, K., and Ramamoorthy, S · 2019
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Adaptive Gaussian copula ABC
Chen, Y. and Gutmann, M. U · 2019
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Efficient Bayesian experimental design for implicit models
Kleinegesse, S. and Gutmann, M. U · 2019
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Monte Carlo Gradient Estimation in Machine Learning
Mohamed, S., Rosca, M., Figurnov, M., and Mnih, A · 2019
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Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
Papamakarios, G., Sterratt, D., and Murray, I · 2019
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On variational bounds of mutual information
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., and Tucker, G · 2019
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Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Kleinegesse, S., Drovandi, C., and Gutmann, M. U · 2020
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