Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure.  Only by carefully modeling this structure can we take fully advantage of the data -- big data must be complemented with big models and the algorithms that can fit them.  Stan is a platform for facilitating this modeling, providing an expressive modeling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.

In this course, https://events.eventzilla.net/e/principled-bayesian-modeling-with-stan-2138610063, Michael Betancourt presents a modern perspective on Bayesian modeling, beginning with a principled Bayesian workflow and then progressing to in depth reviews of popular modeling techniques.  The course emphasizes interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan.

The course consists of six modules each covering a different topic. Each module is are offered in parallel morning and afternoon (EST) sessions for scheduling flexibility and can be taken independently of each other.  Modules are presented remotely through video conferencing and a dedicated Discord server, with all slides, recordings, and exercises made available to attendees.

Module 1: Probabilistic and Generative Modeling
Monday May 6, Thursday May 9

Module 2: Identifiability and Degeneracy
Monday May 13, Thursday May 16

Module 3: Principled Bayesian Model Development Workflow
Monday May 20, Thursday May 23

Module 4: Foundations of Regression Modeling
Monday June 3, Thursday June 6

Module 5: Hierarchical Modeling
Monday June 10, Thursday June 13

Module 6: Gaussian Process Modeling
Monday June 17, Thursday June 20

For detailed module descriptions and course logistics see the course page at https://events.eventzilla.net/e/principled-bayesian-modeling-with-stan-2138610063.  Questions can also be addressed directly to courses [at] symplectomorphic [dot] com.