Reparameterization and Change of Variables
Stan supports a direct encoding of reparameterizations. Stan also supports changes of variables by directly incrementing the log probability accumulator with the log Jacobian of the transform. Theoretical and practical background A Bayesian posterior is technically a probability measure, which is a parameterization-invariant, abstract mathematical object.1 Stan’s modeling language, on the other ha
Stan Docs - Stan User’s Guide
This is the official user’s guide for Stan. It provides example models and programming techniques for coding statistical models in Stan. Part 1 gives Stan code and discussions for several important classes of models. Part 2 discusses various general Stan programming techniques that are not tied to any particular model. Part 3 introduces algorithms for calibration and model checking that require mu
Stan Docs - Stan Functions Reference
This is the reference for the functions defined in the Stan math library and available in the Stan programming language. For more information the Stan language and inference engines and how to use Stan for Bayesian inference, see the Stan User’s Guide. The Stan user’s guide provides example models and programming techniques for coding statistical models in Stan. It also serves as an example-driven
Running Stan on the GPU with OpenCL
Running Stan on the GPU with OpenCL Rok Češnovar and Jonah Gabry Source: vignettes/opencl.Rmd Introduction This vignette demonstrates how to use the OpenCL capabilities of CmdStan with CmdStanR. The functionality described in this vignette requires CmdStan 2.26.1 or newer. As of version 2.26.1, users can expect speedups with OpenCL when using vectorized probability distribution functions (function
Case Studies
open-source methods and models The case studies on this page are intended to reflect best practices in Bayesian methodology and Stan programming. We aim to keep them current with the latest version of the Stan language, but there may be times when case studies need updating to reflect the latest Stan features and syntax. Contributing Case Studies To contribute a case study, please contact us throu
Predator-Prey Population Dynamics: the Lotka-Volterra model in Stan
Plot of the number of lynx and hare pelts (in thousands) collected by the Hudson’s Bay Company between 1900 and 1920. Plot of the number of pelts collected for lynx versus hares from 1900 to 1920. This plot is similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum). The plot makes it clear that the spikes in the lynx population lag those in the hare population. Whe
Stan
Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Many thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business. Stan interfaces with the most popular data analysis languages (R, Python, shell, MATLAB, Julia, Stata) and runs on all majo
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Using the Stan Compiler
Bayesian Statistics Using Stan
Plotting for Bayesian Models
Stan - Case Studies
Workshops: Vanderbilt 2016