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Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations.
“Unit testing for MCMC and other Monte Carlo methods.” URL https://arxiv.org/abs/2001.06465
Gandy, A. and Scott, J. (2020) · 2001
Earlier work this paper cites.
“Getting it right.”
Geweke, J. (2004) · 2004
Earlier work this paper cites.
“Validation of software for Bayesian models using posterior quantiles.”
Cook, S. R., Gelman, A., and Rubin, D. B. (2006) · 2006
Earlier work this paper cites.
“Bayesian workflow.” URL https://arxiv.org/abs/2011.01808
Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., and Modrák, M. (2020) · 2011
Earlier work this paper cites.
“Diagnostic tools for approximate Bayesian computation using the coverage property.”
Prangle, D., Blum, M. G. B., Popovic, G., and Sisson, S. A. (2014) · 2014
Earlier work this paper cites.
“Measuring the reliability of MCMC inference with bidirectional Monte Carlo.”
Grosse, R. B., Ancha, S., and Roy, D. M. (2016) · 2016
Earlier work this paper cites.
“Stan: A probabilistic programming language.”
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017) · 2017
Earlier work this paper cites.
“AIDE: An Algorithm for Measuring the Accuracy of Probabilistic Inference Algorithms.”
Cusumano-Towner, M. F. and Mansinghka, V. K. (2017) · 2017
Earlier work this paper cites.
“Yes, but did it work?: Evaluating variational inference.”
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) · 2018
Earlier work this paper cites.
“Visualization in Bayesian workflow.”
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019) · 2019
Earlier work this paper cites.
“Calibration procedures for approximate Bayesian credible sets.”
Lee, J. E., Nicholls, G. K., and Ryder, R. J. (2019) · 2019
Earlier work this paper cites.
“A family of exact goodness-of-fit tests for high-dimensional discrete distributions.”
Saad, F. A., Freer, C. E., Ackerman, N. L., and Mansinghka, V. K. (2019) · 2019
Cited alongside, same era.
“BayesFlow: Learning complex stochastic models with invertible neural networks.”
Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U. (2020) · 2020
Cited alongside, same era.
“Validating Bayesian inference algorithms with simulation-based calibration.” URL http://www.stat.columbia.edu/~gelman/research/unpublished/sbc.pdf
Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2020) · 2020
Cited alongside, same era.
“Bayesian regression using a prior on the model fit: The R2-D2 shrinkage prior.”
Zhang, Y. D., Naughton, B. P., Bondell, H. D., and Reich, B. J. (2020) · 2020
Cited alongside, same era.
Domke, J. (2021) · 2021
“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
Later among the works it cites.
“Diagnostics for conditional density models and Bayesian inference algorithms.”
Zhao, D., Dalmasso, N., Izbicki, R., and Lee, A. B. (2021) · 2021
Later among the works it cites.
“Testing Whether a Learning Procedure is Calibrated.”
Cockayne, J., Graham, M. M., Oates, C. J., Sullivan, T. J., and Teymur, O. (2022) · 2022
Closest in time.
“SBC: Simulation based calibration for rstan/cmdstanr models.” URL https://github.com/hyunjimoon/SBC/
Kim, S., Moon, A. H., Modrák, M., and Säilynoja, T. (2022) · 2022
Closest in time.
“GATSBI: Generative Adversarial Training for Simulation-Based Inference.”
Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, Á., Greenberg, D. S., Goncalves, P. J., and Macke, J. H. (2022) · 2022
Closest in time.
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Cited alongside, same era.
“Bayesian workflow for disease transmission modeling in Stan.”
Grinsztajn, L., Semenova, E., Margossian, C. C., and Riou, J. (2021) · 2021
Cited alongside, same era.
“Extracting and visualizing tidy residuals from Bayesian models.” URL http://mjskay.github.io/tidybayes/articles/tidybayes-residuals.html
Kay, M. (2021) · 2021
Cited alongside, same era.
“Benchmarking simulation-based inference.”
Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Macke, J. (2021) · 2021
Cited alongside, same era.
“Validating Gaussian process models with simulation-based calibration.”
Mcleod, J. and Simpson, F. (2021) · 2021
Cited alongside, same era.
“Amortized Bayesian model comparison with evidential deep learning.”
Radev, S. T., D’Alessandro, M., Mertens, U. K., Voss, A., Köthe, U., and Bürkner, P.-C. (2021) · 2021
Cited alongside, same era.
“Rank-normalization, folding, and localization: An improved R ^ \widehat{R} for assessing convergence of MCMC (with discussion).”
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C. (2021) · 2021
Cited alongside, same era.
“Discovering inductive bias with Gibbs priors: A diagnostic tool for approximate Bayesian inference.”
Rendsburg, L., Kristiadi, A., Hennig, P., and Von Luxburg, U. (2022) · 2022
Closest in time.
“Graphical Test for Discrete Uniformity and Its Applications in Goodness-of-Fit Evaluation and Multiple Sample Comparison.”
Säilynoja, T., Bürkner, P.-C., and Vehtari, A. (2022) · 2022
Closest in time.
“Workflow techniques for the robust use of Bayes factors.”
Schad, D. J., Nicenboim, B., Bürkner, P.-C., Betancourt, M., and Vasishth, S. (2022) · 2022
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“Intuitive joint priors for Bayesian linear multilevel models: The R2D2M2 prior.”
Aguilar, J. E. and Bürkner, P.-C. (2023) · 2023
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“JANA: Jointly amortized neural approximation of complex Bayesian models.”
Radev, S. T., Schmitt, M., Pratz, V., Picchini, U., Köthe, U., and Bürkner, P.-C. (2023) · 2023
Closest in time.