Project Activities
People and institutions involved
IES program contact(s)
Products and publications
ERIC Citations: Find available citations in ERIC for this award here.
Project Website: https://mc-stan.org/; https://mc-stan.org/about/funding/index.html
Additional Online Resources and Information:
- Stan interfaces for R (cmdstanR at https://mc-stan.org/cmdstanr/) and Python (cmstanPy at https://mc-stan.org/cmdstanpy/)
- Case studies on Bayesian latent class models and handling of label switching (https://mc-stan.org/users/documentation/case-studies/Latent_class_case_study.html) and on multilevel regression and poststratification (https://bookdown.org/jl5522/MRP-case- studies/)
Select Publications:
Broderick, T., Gelman, A., Meager, R., Smith, A. L., & Zheng, T. (2023). Toward a taxonomy of trust for probabilistic machine learning. Science Advances 9, eabn3999.
Gao, Y., Kennedy, L., Simpson, D. & Gelman, A. (2021). Improving multilevel regression and poststratification with structured priors. Bayesian Analysis 16, 719-744.
Gelman, A. (2022). Criticism as asynchronous collaboration: An example from social science research. Stat 11, e464.
Gelman, A., Hullman, J., Wlezien, C., & Morris, G. E. (2020). Information, incentives, and goals in election forecasts. Judgment and Decision Making 15, 863-880.
Gelman, A. & Kennedy, L. (2021). Know your population and know your model: Using model-based regression and post-stratification to generalize findings beyond the observed sample. Psychological Methods 26, 547-558.
Gelman, A., & Vákár, M. (2021) Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data. Statistics in Medicine 40, 3403-3424.
Gin, B., Sim, N., Skrondal, A. and Rabe-Hesketh, S. (2020). A dyadic IRT model. Psychometrika 85, 815-836.
Heidemanns, M., Gelman, A. and Morris, G. E. (2020). An updated dynamic Bayesian forecasting model for the US presidential election. Harvard Data Science Review 2(4). https://doi.org/10.1162/99608f92.fc62f1e1.
Kennedy, L., Simpson, S., & Gelman, A. (2019). The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modeling. Computational Brain and Behavior 2, 210-217.
McShane, B.B., Gal, D., Gelman, A., Robert, C., & Tackett, J.L. (2019). Abandon statistical significance. American Statistician 73(S1), 235-245.
Merkle, E. C., Fitzsimmons, E., Uanhoro, J., and Goodrich, B. (2022). Efficient Bayesian structural equation modeling in Stan. Journal of Statistical Software 100(6), 1-22.
Merkle, E., Furr, D., & Rabe-Hesketh, S. (2019). Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods. Psychometrika 84, 802-829.
Vehtari, A., Gelman, A, Simpson D., Carpenter, B., & Bürkner, P. (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. Bayesian Analysis 16, 667-718.
Vehtari, A., Gelman, A., Sivula T., Jylanki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J., Schiminovich, D., & Robert, C. (2020). Expectation propagation as a way of life: A framework for Bayesian inference on partioned data. Journal of Machine Learning Research 21, 1-53. ED634110
Yao, Y., Vehtari, A., & Gelman, A. (2022). Stacking for non-mixing Bayesian computations: The curse and blessing of multimodal posteriors. Journal of Machine Learning Research 23, 79.
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Supplemental information
Co-Principal Investigator: Rabe-Hesketh, Sophia
- Code that allows Stan to analyze data for a wider variety of models (https://mc-stan.org/).
- Stan interfaces for R and Python that make Stan more accessible to a larger group of users and allow software developers to more easily create wrappers making Stan programs accessible to R and Python users who are not Stan users themselves (cmdstanR at https://mc-stan.org/cmdstanr/) and Python (cmstanPy at https://mc-stan.org/cmdstanpy/).
- Case studies on Bayesian latent class models and handling of label switching (https://mc-stan.org/users/documentation/case-studies/Latent_class_case_study.html) and on multilevel regression and poststratification (https://bookdown.org/jl5522/MRP-case- studies/).
Statistical/Methodological Product: Stan is an open-source Bayesian inference engine, accessible from Python, R, Julia, Stata, and other statistics and mathematics languages. Stan is created for a range of users, from statisticians and computer scientists to applied researchers in science, engineering, government, and business. Stan is widely used in education research, especially for fitting latent-variable models in hierarchical or multilevel data structures that arise in program evaluation, policy analysis, and item-response modeling.
Development/Refinement Process: The project team developed algorithms, tested them on a range of theoretical and applied examples, and developed user-friendly tools and documentation, using a range of datasets.
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