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Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator models with intractable likelihood.
Strictly proper scoring rules, prediction, and estimation
T. Gneiting and A. E. Raftery · 2007
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Probabilistic forecasts, calibration and sharpness
T. Gneiting, F. Balabdaoui, and A. E. Raftery · 2007
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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DISCO nets: DISsimilarity COefficient networks
D. Bouchacourt, P. K. Mudigonda, and S. Nowozin · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Fast ε \varepsilon -free inference of simulation models with Bayesian conditional density estimation
G. Papamakarios and I. Murray · 2016
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Energy distance
M. L. Rizzo and G. J. Székely · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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EMNIST: Extending MNIST to handwritten letters
G. Cohen, S. Afshar, J. Tapson, and A. Van Schaik · 2017
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Fundamentals and recent developments in approximate Bayesian computation
J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander · 2017
Cited alongside, same era.
Flexible statistical inference for mechanistic models of neural dynamics
J.-M. Lueckmann, P. J. Goncalves, G. Bassetto, K. Öcal, M. Nonnenmacher, and J. H. Macke · 2017
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Demystifying MMD GANs
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
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Bayesian synthetic likelihood
L. F. Price, C. C. Drovandi, A. Lee, and D. J. Nott · 2018
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On GANs and GMMs
E. Richardson and Y. Weiss · 2018
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Validating bayesian inference algorithms with simulation-based calibration
S. Talts, M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman · 2018
Robust Bayesian synthetic likelihood via a semi-parametric approach
Z. An, D. J. Nott, and C. Drovandi · 2020
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MMD-Bayes: Robust Bayesian estimation via maximum mean discrepancy
B.-E. Chérief-Abdellatif and P. Alquier · 2020
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On contrastive learning for likelihood-free inference
C. Durkan, I. Murray, and G. Papamakarios · 2020
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A spectral energy distance for parallel speech synthesis
A. A. Gritsenko, T. Salimans, R. v. d. Berg, J. Snoek, and N. Kalchbrenner · 2020
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Approximate Bayesian computation via the energy statistic
H. D. Nguyen, J. Arbel, H. Lü, and F. Forbes · 2020
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BayesFlow: Learning complex stochastic models with invertible neural networks
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Cited alongside, same era.
Approximate Bayesian computation with the Wasserstein distance
E. Bernton, P. E. Jacob, M. Gerber, and C. P. Robert · 2019
Cited alongside, same era.
Automatic posterior transformation for likelihood-free inference
D. Greenberg, M. Nonnenmacher, and J. Macke · 2019
Cited alongside, same era.
Likelihood-free inference with emulator networks
J.-M. Lueckmann, G. Bassetto, T. Karaletsos, and J. H. Macke · 2019
Cited alongside, same era.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
G. Papamakarios, D. Sterratt, and I. Murray · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Cited alongside, same era.
S. T. Radev, U. K. Mertens, A. Voss, L. Ardizzone, and U. Köthe · 2020
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Estimating and evaluating regression predictive uncertainty in deep object detectors
A. Harakeh and S. L. Waslander · 2021
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Benchmarking simulation-based inference
J.-M. Lueckmann, J. Boelts, D. Greenberg, P. Goncalves, and J. Macke · 2021
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Normalizing flows for probabilistic modeling and inference
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan · 2021
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Probabilistic forecasting with conditional generative networks via scoring rule minimization
L. Pacchiardi, R. Adewoyin, P. Dueben, and R. Dutta · 2022
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GATSBI: Generative adversarial training for simulation-based inference
P. Ramesh, J.-M. Lueckmann, J. Boelts, Á. Tejero-Cantero, D. S. Greenberg, P. J. Goncalves, and J. H. Macke · 2022
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