Upcoming Remote Stan Courses

I forgot to mention that I’ll be giving remote Stan courses again next year starting in February, Principled Statistical Modeling with Stan - Powered by Eventzilla. Each course consists of a live lecture, interactive exercises in RStan and PyStan, a live exercise review, and dedicated Discord discussion channel. In addition to the exercises each attendee will also receive recordings of the live sessions and a copy of the slides so that you can even follow along if you can’t attend live.

The course material is on the more advanced side, but Jumping Rivers will also be given more introductory Stan courses leading up to these courses; for course details go to Data Science Training Courses and click on “Stan” on the left to filter.

Each course can be taken separately, and each is given in both in the EST morning and afternoon sessions.

Module 1: Probabilistic and Generative Modeling

Module 1 discusses probabilistic modeling with an emphasis on generative models that capture the particular details of an assumed data generating process. The first lecture reviews the conceptual foundations of probabilistic modeling and statistical inference while the second lecture focuses on the powerful practical benefits of generative modeling.

Module 2: Identifiability and Degeneracy

The second module introduces the quantitative concept of the identifiability of probabilistic models and the more qualitative, but more practically relevant, concept of degeneracy of statistical inferences. We will review strategies for not only identifying and investigating degenerate behavior but also managing the pathological consequences of that behavior.

Module 3: Principled Bayesian Workflow

In this module we review a principled Bayesian workflow that guides the development of statistical models suited to the particular details of a given application. The workflow integrates the development of prior models, computational calibration, inferential calibration, and model critique and model updating.

Module 4: Regression Modeling

This module presents linear and general linear regression techniques from a modeling perspective, using that context to motivate robust implementations. We will especially emphasize principled prior modeling strategies for linear, log, and logistic regression models.

Module 5: Hierarchical Modeling

Module 5 introduces exchangeability and hierarchical models with a strong focus on the inherent identifiability issues and their computational consequences, as well as strategies for moderating this issues.

Module 6: Factor Modeling

This module introduces conditional exchangeability, marginal exchangeability, and observed factor modeling (also known as multilevel or random effects modeling) with a focus on efficient implementations.

Completion of Module 5 is highly recommended.

Module 7: Gaussian Process Modeling

The seventh module introduces Gaussian processes as a statistical modeling technique, motivating principled prior models that avoid pathological behavior.

Module 8: Sparsity Modeling

The final module reviews the concept of sparsity in Bayesian inference and prior modeling techniques that encourage sparse inferences.

Completion of Module 5 is highly recommended.

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