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. Probabilistic programming languages like Stan facilitating this modeling, allowing us to implement bespoke models and while providing state-of-the-art algorithms to compute Bayesian inferences.
In these courses, Advanced Bayesian Modeling In Stan - Powered by Eventzilla, Michael Betancourt presents a series of advanced Bayesian modeling techniques and their implementation in a principled Bayesian workflow, including discussions of prior modeling, inferential degeneracies, and more. Each course module incorporates interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan.
The course consists of four 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: Mixture Modeling
Monday August 18, Thursday August 21
Module 2: Survival Modeling
Monday August 25, Thursday August 28
Module 3: Pairwise Comparison Modeling
Monday September 15, Thursday September 18
Module 4: Ordinal Modeling
September 22,Thursday September 25
For detailed module descriptions and course logistics see the course page at Advanced Bayesian Modeling In Stan - Powered by Eventzilla. Questions can also be addressed directly to courses [at] symplectomorphic [dot] com.