2023 Remote Courses

Just wanted to share that I’ll be giving remote Stan courses again next year starting in February, Principled Bayesian 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 for future reference or in order to follow along if attendance isn’t possible.

Each course module can be taken separately, and each is given in both in the EST morning and afternoon sessions. If you have any questions don’t hesitate to reach out.

The Modeling Suite

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 techniques for developing bespoke generative models.

Module 2: Identifiability and Degeneracy

The second module concerns uncertainty, introducing 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 Model Development Workflow

In this module we review a workflow that guides the development of Bayesian models suited to the particular details of a given application. The workflow integrates the development of prior model elicitation, experimental design, model critique and model updating.

The Regression Suite

Module 4: Variate Covariate Modeling

This module considers regression from a Bayesian modeling perspective, investigating the crucial but often overlooked assumptions inherent to these techniques. The discussion will place a particular focus on the inferential consequences of confouding.

Module 5: Taylor Modeling

Module 5 introduces linear regression as a local approximation of more general regression analyses. The theory of Taylor approximations offers interpretability and guidance for how these linear models, and the heuristics that often accompany them, can be robustly applied in practice.

Module 6: General Taylor Modeling

In this module we will combine Taylor approximation with unconstraining transformations to build robust models for regression analyses with constraints. We will consider the special cases of exponential and logistic regression, including the development of principled prior models for theses analyses.

The Heterogeneity Suite

Module 7: Hierarchical Modeling

Module 7 introduces exchangeability and hierarchical models for heterogeneous data generating processes. We will place a strong focus on the inherent identifiability issues and their computational consequences, as well as strategies for moderating this issues.

Module 8: 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 robust and efficient implementations.

Module 9: Sparsity Modeling

The final module reviews the concept of sparsity in Bayesian inference and hierarchical modeling techniques that can encourage sparse inferences of heterogeneous behavior.