StanCon 2023 June 20-21, at the Washington University in St. Louis was a great success.
Slides and materials from the talks and presentations are available at https://github.com/stan-dev/stancon2023
We thank our sponsors for their generosity, all of the speakers for sharing their expertise, the StanCon organizing committee and the local organizers for many, many hours of hard work, and the WUSTL staff for their hospitality. Many, many thanks to all of the participants for making this a rewarding experience!
Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
Users specify log density functions in Stan’s probabilistic programming language and get:
full Bayesian statistical inference with MCMC sampling (NUTS, HMC)
approximate Bayesian inference with variational inference (ADVI)
penalized maximum likelihood estimation with optimization (L-BFGS)
Stan’s math library provides differentiable probability functions & linear algebra (C++ autodiff). Additional R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation.
Stan interfaces with the most popular data analysis languages (R, Python, shell, MATLAB, Julia, Stata) and runs on all major platforms (Linux, Mac, Windows). To get started using Stan begin with the Installation and Documentation pages.
We appreciate citations for the Stan software because it lets us find out what people have been doing with Stan and motivate further grant funding. See How to Cite Stan for more details.
Open Code & Reproducible Science
Stan is freedom-respecting, open-source software (new BSD core, some interfaces GPLv3). Stan is associated with NumFOCUS, a 501(c)(3) nonprofit supporting open code and reproducible science, through which you can help support Stan.