References

Akaike, H. 1974. “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control 19 (6): 716–23. https://doi.org/10.1109/TAC.1974.1100705.
Bessiere, Pierre, Emmanuel Mazer, Juan Manuel Ahuactzin, and Kamel Mekhnacha. 2013. Bayesian Programming. 1 edition. Boca Raton: Chapman; Hall/CRC. https://www.crcpress.com/Bayesian-Programming/Bessiere-Mazer-Ahuactzin-Mekhnacha/p/book/9781439880326.
Brockmann, H. Jane. 1996. “Satellite Male Groups in Horseshoe Crabs, Limulus Polyphemus.” Ethology 102 (1): 1–21. https://doi.org/https://doi.org/10.1111/j.1439-0310.1996.tb01099.x.
Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98. https://doi.org/10.1214/09-AOAS285.
Cleveland, William S., and Robert McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79 (387): 531–54. https://doi.org/10.1080/01621459.1984.10478080.
Daniel Roy. 2015. Probabilistic Programming. http://probabilistic-programming.org.
Diaconis, Persi. 2011. “Theories of Data Analysis: From Magical Thinking Through Classical Statistics.” In Exploring Data Tables, Trends, and Shapes, 1–36. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118150702.ch1.
Gelman, Andrew, Daniel Simpson, and Michael Betancourt. 2017. “The Prior Can Often Only Be Understood in the Context of the Likelihood.” Entropy 19 (10): 555. https://doi.org/10.3390/e19100555.
Gelman, Andrew, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. 2020. “Bayesian Workflow.” https://arxiv.org/abs/2011.01808.
Ghahramani, Zoubin. 2015. “Probabilistic Machine Learning and Artificial Intelligence.” Nature 521 (7553): 452–59. https://doi.org/10.1038/nature14541.
Greenhill, Brian, Michael D. Ward, and Audrey Sacks. 2011. “The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.” American Journal of Political Science 55 (4): 991–1002. https://doi.org/https://doi.org/10.1111/j.1540-5907.2011.00525.x.
Heer, Jeffrey, and Michael Bostock. 2010. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 203–12. CHI ’10. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/1753326.1753357.
Hoyer, Stephan, and Joe Hamman. 2017. “Xarray: N-D Labeled Arrays and Datasets in Python.” Journal of Open Research Software 5 (1). https://doi.org/10.5334/jors.148.
Icazatti, Alejandro, Oriol Abril-Pla, Arto Klami, and Osvaldo A Martin. 2023. PreliZ: A tool-box for prior elicitation.” Journal of Open Source Software 8 (89): 5499. https://doi.org/10.21105/joss.05499.
Jaynes, E. T. 2003. Probability Theory: The Logic of Science. Edited by G. Larry Bretthorst. Cambridge, UK ; New York, NY: Cambridge University Press.
Kallioinen, Noa, Topi Paananen, Paul-Christian Bürkner, and Aki Vehtari. 2023. “Detecting and Diagnosing Prior and Likelihood Sensitivity with Power-Scaling.” Statistics and Computing 34 (1): 57. https://doi.org/10.1007/s11222-023-10366-5.
Kleiber, Christian, and Achim Zeileis. 2016. “Visualizing Count Data Regressions Using Rootograms.” The American Statistician 70 (3): 296–303. https://doi.org/10.1080/00031305.2016.1173590.
Link, William A., and Mitchell J. Eaton. 2012. “On Thinning of Chains in MCMC.” Methods in Ecology and Evolution 3 (1): 112–15. https://doi.org/https://doi.org/10.1111/j.2041-210X.2011.00131.x.
MacEachern, Steven N., and L. Mark Berliner. 1994. “Subsampling the Gibbs Sampler.” The American Statistician 48 (3): 188–90. https://doi.org/10.2307/2684714.
Martin, Osvaldo A. 2024. Bayesian Analysis with Python: A Practical Guide to Probabilistic Modeling, 3rd Edition. Packt Publishing.
Martin, Osvaldo A., Ravin Kumar, and Junpeng Lao. 2021. Bayesian Modeling and Computation in Python. 1st edition. Boca Raton London New York: Chapman; Hall/CRC.
McLatchie, Yann, Sölvi Rögnvaldsson, Frank Weber, and Aki Vehtari. 2023. “Robust and Efficient Projection Predictive Inference.” https://arxiv.org/abs/2306.15581.
Mikkola, Petrus, Osvaldo A. Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, et al. 2024. Prior Knowledge Elicitation: The Past, Present, and Future.” Bayesian Analysis 19 (4): 1129–61. https://doi.org/10.1214/23-BA1381.
Morris, David E., Jeremy E. Oakley, and John A. Crowe. 2014. “A Web-Based Tool for Eliciting Probability Distributions from Experts.” Environmental Modelling & Software 52: 1–4. https://doi.org/https://doi.org/10.1016/j.envsoft.2013.10.010.
Piironen, Juho, Markus Paasiniemi, and Aki Vehtari. 2020. Projective inference in high-dimensional problems: Prediction and feature selection.” Electronic Journal of Statistics 14 (1): 2155–97. https://doi.org/10.1214/20-EJS1711.
Quiroga, Miriana, Pablo G Garay, Juan M. Alonso, Juan Martin Loyola, and Osvaldo A Martin. 2022. “Bayesian Additive Regression Trees for Probabilistic Programming.” arXiv. https://doi.org/10.48550/ARXIV.2206.03619.
Säilynoja, Teemu, Paul-Christian Bürkner, and Aki Vehtari. 2022. “Graphical Test for Discrete Uniformity and Its Applications in Goodness-of-Fit Evaluation and Multiple Sample Comparison.” Statistics and Computing 32 (2): 32. https://doi.org/10.1007/s11222-022-10090-6.
Talts, Sean, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman. 2020. “Validating Bayesian Inference Algorithms with Simulation-Based Calibration.” https://arxiv.org/abs/1804.06788.
Tukey, John W. 1977. Exploratory Data Analysis. 1 edition. Pearson.
Vehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. “Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC.” Statistics and Computing 27 (5): 1413–32. https://doi.org/10.1007/s11222-016-9696-4.
Vehtari, Aki, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. 2021. Rank-Normalization, Folding, and Localization: An Improved for Assessing Convergence of MCMC (with Discussion).” Bayesian Analysis 16 (2): 667–718. https://doi.org/10.1214/20-BA1221.
Watanabe, Sumio. 2013. “A Widely Applicable Bayesian Information Criterion.” Journal of Machine Learning Research 14 (March): 867–97.
Yao, Yuling, Aki Vehtari, Daniel Simpson, and Andrew Gelman. 2018. Using Stacking to Average Bayesian Predictive Distributions (with Discussion).” Bayesian Analysis 13 (3): 917–1007. https://doi.org/10.1214/17-BA1091.