Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity-Reference-Cited by-同舟云学术

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity

Published:2023-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Modrák Martin¹,Moon Angie H.²,Kim Shinyoung³,Bürkner Paul⁴,Huurre Niko,Faltejsková Kateřina⁵,Gelman Andrew⁶,Vehtari Aki⁷

Affiliation:

1. Institute of Microbiology of the Czech Academy of Sciences

2. Massachusetts Institute of Technology

3. Department of Computer Science, Kookmin University

4. Department of Statistics, Technical University of Dortmund

5. Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences

6. Department of Statistics and Department of Political Science, Columbia University

7. Department of Computer Science, Aalto University

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference33 articles.

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2. Cook, S. R., Gelman, A., and Rubin, D. B. (2006). “Validation of Software for Bayesian Models Using Posterior Quantiles.” Journal of Computational and Graphical Statistics, 15(3): 675–692.

3. Aguilar, J. E. and Bürkner, P.-C. (2023). “Intuitive joint priors for Bayesian linear multilevel models: The R2D2M2 prior.” Electronic Journal of Statistics, 17(1): 1711–1767.

4. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017). “Stan: A probabilistic programming language.” Journal of Statistical Software, 76(1). URL https://www.jstatsoft.org/index.php/jss/article/view/v076i01

5. Cockayne, J., Graham, M. M., Oates, C. J., Sullivan, T. J., and Teymur, O. (2022). “Testing whether a learning procedure is calibrated.” Journal of Machine Learning Research, 23(203): 1–36. URL http://jmlr.org/papers/v23/21-1065.html

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