Dear Colleagues,
In case you are interested in adopting a modern perspective on Bayesian modeling, I am sharing some information below on a six-module course offered online by Michael Betancourt between May 6th and June 20th, 2024. The course is titled "Principled Bayesian Modeling with Stan".
Michael is a brilliant scientist who is always generous with his statistical insights on Twitter, where I follow him. His guidance impacts how others do research; for example, this article on ecological forecasting by Nick Clark and collaborators relied on Michael's guidance to develop bespoke checking functions to replace generic criteria (AIC, DIC) for targeted model expansion in the context of Bayesian Dynamic GAMs: https://www.sciencedirect.com/science/article/pii/S0304380024000371.
Here is the promised information on the course:
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 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
The contact information for the course instructor is as follows:
Michael Betancourt
Principal Research Scientist
Symplectomorphic, LLC
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I hope some of you will find the above information useful. With the fragmentation of the statistical community on Twitter (now X), it is now harder to get the word out about courses like these.
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For those of you curious to read Michael's perspective on Bayesian modeling, here is a link to his writing:
https://betanalpha.github.io/writing/
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Thank you and be well,
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Isabella Ghement
Ghement Statistical Consulting Company Ltd.
E-mail: isabella@ghement.ca
Phone: 604-767-1250