- Dates: 11-15 September 2023.
- Times: 9AM-5PM daily.
- Location: The summer school will be held at the Griebnitzsee campus in Potsdam, at Haus 6. For train connections, consult bvg.de; the train station near the campus is called Griebnitzsee Bhf.
**Application period: 30 Sept 2022 to 1 April 2023**. Click here to apply. Decisions will be announced around 15 April 2023.

**Special short course: Introduction to Bayesian meta-analysis**. Taught by Gian Luca Di Tanna.

Timing: Tuesday and Thursday: 3:00-4:30PM. Anyone can attend this short course.

**Introduction to Bayesian data analysis**(maximum 30 participants). Taught by Himanshu Yadav, assisted by Anna Laurinavichyute.**Advanced Bayesian data analysis**(maximum 30 participants). Taught by Bruno Nicenboim
This course assumes that participants have some experience in Bayesian modeling already using brms and want to transition to Stan to learn more advanced methods and start building simple computational cognitive models. Participants should have worked through or be familiar with the material in the first five chapters of our book draft: Introduction to Bayesian Data Analysis for Cognitive Science. In this course, we will cover Parts III to V of our book draft: model comparison using Bayes factors and k-fold cross validation, introduction and relatively advanced models with Stan, and simple computational cognitive models.
**Foundational methods in frequentist statistics**(maximum 30 participants). Taught by Audrey Buerki, Daniel Schad, and João Veríssimo.
Participants will be expected to have used linear mixed models before, to the level of the textbook by Winter (2019, Statistics for Linguists), and want to acquire a deeper knowledge of frequentist foundations, and understand the linear mixed modeling framework more deeply. Participants are also expected to have fit multiple regressions. We will cover model selection, contrast coding, with a heavy emphasis on simulations to compute power and to understand what the model implies. We will work on (at least some of) the participants' own datasets. **Advanced methods in frequentist statistics with Julia**(maximum 30 participants). Taught by Reinhold Kliegl, Phillip Alday, and (over zoom:) Doug Bates.

This course is an introduction to Bayesian modeling, oriented towards linguists and psychologists. Topics to be covered: Introduction to Bayesian data analysis, Linear Modeling, Hierarchical Models. We will cover these topics within the context of an applied Bayesian workflow that includes exploratory data analysis, model fitting, and model checking using simulation.