This course provides a general, application-oriented introduction to Bayesian data anlysis. The course lays the foundation for "thinking like a Bayesian" by introducing explicit models of the data-generating process. It provides an overview of the basic concepts of Bayesian data analysis to the extent necessary for common applications. Using practical examples and hands-on exercises, the course works towards application-level confidence in the use of simple and generalized Bayesian regression models.

The purpose of this course is for everyone to extend their comfort zone for "applied Bayes" in some way or another, thus appealing to beginning and intermediate level prior knowledge. The course also contains interactive practice sessions during which participants can bring up their own concrete use cases and question sets.

The course is intended as both a first introduction to Bayesian data analysis, but also to serve as a consolidation and further guide for anyone with some prior experience BDA and Bayesian regression modeling.
It assumes basic familiarity with R.
The course will also make use of the tidyverse (https://www.tidyverse.org/).
Course material will be similar to Part III of this introductory web-book, and Chapters 1-3 of this applied web-book [2], with some practically relevant excerpts from Chapters 4 and 5 as well.

After completing this course, the participants will:

1. have become familiar with the basics of Bayesian inference,

2. be able to fit a range of regression models with several likelihood functions,

3. be able fit several robust models and distributional models,

4. know how to select priors for their models using prior predictive checks,

5. know how to assess the descriptive accuracy of a model using posterior predictive checks, and

6. know how to express their posterior distributions as effect sizes and informative figures.

Before class, please install the latest version of R from the Comprehensive R Archive Network (CRAN). Choose the appropriate "Download R" link depending on your operating system and follow the instructions for downloading and installing R. If you already have R installed, please check that it is the current version (you can check what the 'latest release' of R is by going to CRAN and then compare this with the version shown when you start R). If you do not have the latest version installed, please update.

Although not strictly necessary, it will be useful to also install an integrated development environment (IDE) for R. A popular choice is RStudio. So, unless you already have a different setup, please download and install RStudio.

Please also install the following R packages (ideally: latest versions available):
- tidyverse
- brms

It is also recommended that you install the package 'cmdstanr'.

As an intuition pump for thinking about generative stochastic processes, the course will use the easy, light-weight probabilistic programming language WebPPL. It is not important to know WebPPL, and it is not necessary to install anything, as WebPPL programs can be executed from the browser.

*** Monday - 2-6 pm Berlin time
1. Introduction, practicalities, course overview
2. Basics of Bayesian data analysis (BDA)
3. Generative-process models with WebPPL

*** Tuesday - 2-6 pm Berlin time
1. Non-technical "all you need to know" tutorial on MCMC methods
2. Bayesian parameter inference with MCMC methods
3. Applications: inferring means and simple linear regression coefficients

*** Wednesday- 2-6 pm Berlin time
1. Model checking: prior and posterior predictive checks
2. Applications: Generalized linear models (logistic, ordered logit, multinomial)

*** Thursday- 2-6 pm Berlin time
1. Bayesian model comparison (Bayes Factors and cross-validation)
2. Multi-level modeling: why and how?

*** Friday- 2-6 pm Berlin time
1. Good practices: Bayesian workflow, how to report results, ...
2. [topics of interest to the audience / general discussion]

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.