Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and o...

Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. ...

The corresponding issue #299 This PR is not ready for merge, but needs some thought

It is useful to estimate the expected predictive performance of models planned to be used for prediction. We focus on leave-one-out cross-validation (LOO-CV), which has become a popular method for estimating predictive performance of Bayesian models. Given two models, we are interested in comparing the predictive performances and associated uncertainty, which can also be used to compute the probability of one model having better predictive performance than the other model. We study the properties of the Bayesian LOO-CV estimator and the related uncertainty quantification for the predictive performance difference, and analyse when a normal approximation of this uncertainty is well calibrated and whether taking into account higher moments could improve the approximation. We provide new results of the properties both theoretically in the linear regression case and empirically for hierarchical linear, latent linear, and spline models and discuss the challenges. We show that problematic cases include: comparing models with similar predictions, misspecified models, and small data. In these cases, there is a weak connection between the distributions of the LOO-CV estimator and its error. We show that that the problematic skewness of the error distribution for the difference, which occurs when the models make similar predictions, does not fade away when the data size grows to infinity in certain situations. Based on the results, we also provide some practical recommendations for the users of Bayesian LOO-CV for comparing predictive performance of models.

We are thrilled to announce that StanCon 2026 will take place in Uppsala, Sweden, from August 17th to August 21st, 2026. The conference brings together researchers and practitioners passionate about Bayesian inference and probabilistic programming in one of Sweden’s most historic and vibrant university cities. Attendees will enjoy a week of conference talks, workshops, and tutorials spanning both foundational methods and real-world applications.

We are thrilled to announce that StanCon 2026 will take place in Uppsala, Sweden, from August 17th to August 21st, 2026. The conference brings together researchers and practitioners passionate about Bayesian inference and probabilistic programming in one of Sweden’s most historic and vibrant university cities. Attendees will enjoy a week of conference talks, workshops, and tutorials spanning both foundational methods and real-world applications.

We are very happy to announce that the 2.37.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. Thi…