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To check the accuracy of Bayesian computations, it is common to use rank-based simulation-based calibration (SBC).
Sufficient statistics and uniformly most powerful tests of statistical hypotheses
Neyman, J. and Pearson, E. (1936) · 1936
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The Bayesian bootstrap
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The central role of the propensity score in observational studies for causal effects
Rosenbaum, P. R. and Rubin, D. B. (1983) · 1983
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Elements of Information Theory
Cover, T. M. and Thomas, J. A. (1991) · 1991
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Inference from iterative simulation using multiple sequences
Gelman, A. and Rubin, D. B. (1992) · 1992
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Practical Markov chain Monte Carlo
Geyer, C. J. (1992) · 1992
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Posterior predictive assessment of model fitness via realized discrepancies
Gelman, A., Meng, X.-L., and Stern, H. (1996) · 1996
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Getting it right: Joint distribution tests of posterior simulators
Geweke, J. (2004) · 2004
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Validation of software for Bayesian models using posterior quantiles
Cook, S. R., Gelman, A., and Rubin, D. B. (2006) · 2006
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On divergences and informations in statistics and information theory
Liese, F. and Vajda, I. (2006) · 2006
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On surrogate loss functions and
Nguyen, X., Wainwright, M. J., and Jordan, M. I. (2009) · 2009
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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) · 2010
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Information, divergence and risk for binary experiments
Reid, M. and Williamson, R. (2011) · 2011
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Valid post-selection inference
Berk, R., Brown, L., Buja, A., Zhang, K., and Zhao, L. (2013) · 2013
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Understanding predictive information criteria for Bayesian models
Gelman, A., Hwang, J., and Vehtari, A. (2014) · 2014
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S. (2014) · 2014
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Diagnostic tools of approximate Bayesian computation using the coverage property
Prangle, D., Blum, M., Popovic, G., and Sisson, S. (2014) · 2014
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f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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Measuring sample quality with kernels
An easy to interpret diagnostic for approximate inference: Symmetric divergence over simulations
Domke, J. (2021) · 2021
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Benchmarking simulation-based inference
Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Macke, J. (2021) · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2021) · 2021
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Neural empirical Bayes: Source distribution estimation and its applications to simulation-based inference
Vandegar, M., Kagan, M., Wehenkel, A., and Louppe, G. (2021) · 2021
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Rank-normalization, folding, and localization: An improved
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C. (2021) · 2021
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Gorham, J. and Mackey, L. (2017) · 2017
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MINE: mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, R. D. (2018) · 2018
Cited alongside, same era.
Validating Bayesian inference algorithms with simulation-based calibration
Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018) · 2018
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Yes, but did it work?: Evaluating variational inference
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) · 2018
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Visualization in bayesian workflow
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019) · 2019
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Advances in black-box VI: Normalizing flows, importance weighting, and optimization
Agrawal, A., Sheldon, D. R., and Domke, J. (2020) · 2020
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The frontier of simulation-based inference
Cranmer, K., Brehmer, J., and Louppe, G. (2020) · 2020
Cited alongside, same era.
Assessment and adjustment of approximate inference algorithms using the law of total variance
Yu, X., Nott, D. J., Tran, M.-N., and Klein, N. (2021) · 2021
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A trust crisis in simulation-based inference? Your posterior approximations can be unfaithful
Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., Begy, V., and Louppe, G. (2022) · 2022
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R ∗ R^{*} : A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers
Lambert, B. and Vehtari, A. (2022) · 2022
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Validation diagnostics for SBI algorithms based on normalizing flows
Linhart, J., Gramfort, A., and Rodrigues, P. L. (2022) · 2022
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Simulation-based inference with WALDO: Perfectly calibrated confidence regions using any prediction or posterior estimation algorithm
Masserano, L., Dorigo, T., Izbicki, R., Kuusela, M., and Lee, A. B. (2022) · 2022
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Modrák, M., Moon, A. H., Kim, S., Bürkner, P., Huurre, N., Faltejsková, K., Gelman, A., and Vehtari, A. (2022) · 2022
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GATSBI: Generative adversarial training for simulation-based inference
Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, Á., Greenberg, D. S., Gonçalves, P. J., and Macke, J. H. (2022) · 2022
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Sampling-based accuracy testing of posterior estimators for general inference
Lemos, P., Coogan, A., Hezaveh, Y., and Perreault-Levasseur, L. (2023) · 2023
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SimBIG: Galaxy clustering analysis with the wavelet scattering transform
Régaldo-Saint Blancard, B., Hahn, C., Ho, S., Hou, J., Lemos, P., Massara, E., Modi, C., Moradinezhad Dizgah, A., Parker, L., , Yao, Y., and Eickenberg, M. (2023) · 2023
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