3 days of lectures and hand-on exercises

Deadline for registration: 12/05/2022

Eager to know more on Probabilistic modelling, identifiability and degeneracy, Bayesian workflows and Hierarchical modelling?

Register for a 3-day workshop organised by the Institute de Neurosciences des Systemes on "Bayesian Data Analysis" with Michael Betancourt, taking place between May 17th to 19th, 2022 in Marseille

The course is taking place live at CAMPUS TIMONE - Faculté de Médecine,

27, Boulevard Jean Moulin, Bâtiment pédagogique, Hall 204, 13005 Marseille, France

Registration fees, covering a 3-day training, amount to €250 per person (including adm. fees).

Description:

Due to the importance and common use of Bayesian framework for inference and prediction, the advanced techniques in probabilistic programming languages to overcome the inference difficulties with big data complemented with big models have been receiving increasing attention in this context. Stan is a popular platform for facilitating inference, providing an expressive modelling language and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.

We are very happy to announce the INS courses on "Bayesian Data Analysis", with Michael Betancourt, a core developer of Stan and expert on Hamilton Monte Carlo. The courses begin by surveying Bayesian inference, Bayesian computation and a principled introduction to Stan.

With a solid foundation, we will move onto to the elements of a robust Bayesian workflow in practice and then continue to the problem of interest to neuroscientist such as source localization in Neuroimaging.

The courses are highly interactive, with exercises demonstrating a principled Bayesian workflow and range of modelling techniques run in Python environment. Courses run for 3 days and include material spanning Probabilistic modelling, identifiability and degeneracy, Bayesian workflows and Hierarchical modelling.

Prerequisites:

The course will assume familiarity with the basics of calculus, linear algebra, and probability theory. For a self-contained introduction to the latter please review my probability theory, conditional probability theory, and common probability densities case studies. The last will be particularly relevant.

Workshop on Bayesian Data Analysis

With Michael Betancourt, taking place between May 17th to 19th, 2022

Salle informatique 204 - bâtiment pédagogique (CAMPUS TIMONE, Faculté de Médecine,

27, Boulevard Jean Moulin - 13005 Marseille, France)

Day 1, May 17th

9:30-12:30

COFFEE BREAK 11:00-11:30

Lecture on probabilistic modelling

- Modelling and inference

- Generative Modeling

13:30-17:00

Exercises on modelling, Poisson model of spike counting

Day 2, May 18th

9:30-12:30

COFFEE BREAK 11:00-11:30

Identifiability and degeneracy lecture

- Robust workflows for Bayesian modelling

- Identifiability and degeneracy

End of morning: examples and discussion

13:30-17:00

Model building Bayesian workflow lecture

- Bayesian model building workflow

- Hands on modelling: MEG/sEEG source analysis

15:00-17:00

Exercises and discussion

The nominal material is based around the Poisson progression in https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html,

Day 3, May 19th

9:30-12:30

COFFEE BREAK 11:00-11:30

Hierarchical modeling lecture and exercises from the first 2 days.

End of morning: examples and discussion

13:30-17:00

Exercises on modelling: temporal models with ODEs

Open discussion with participant models, datasets and questions

Please, note that the training is taking place in the building right next to the entrance of the Campus Timone (bâtiment pédagogique, yellow/green building).