Welcome to the workshop on Bayesian Statistics Using R. This page will be used to distribute the slides, handouts, and other materials for this course. All material is free to re-use under the CC-BY license.

In this workshop, you will receive an in-depth introduction to the Bayesian Framework of Data Analysis. You will learn about the infamous “Bayes Theorem” and how it can be used for “Bayesian Inference”. More practically, you will further learn how to run (generalized) linear models using a Bayesian framework as implemented in the R package brms (Bürckner, 2017). You will learn how to specify models, appropriate priors, and how to interpret respective results. Thereby we will get to know different methods to draw probabilistic inferences from posterier distributions, moving beyond standard Frequentist null hypothesis testing.



Preparation

For this 2-day workshop, I expect you…

  1. to have R and RStudio installed. If you haven’t done so yet, have a look at this getting started tutorial, which walks you through the installation and helps you get some first hands-on experience using R. You do not need to install the packages that we will use in the workship yet. We will install those together in the workshop.

  2. to have some basic knowledge of R and particularly data wrangling skills (at best, some knowledge about the tidyverse). If this is still new to you, we have several videos and tutorials on our R course material GitHub page that can help you getting started. I would suggest to check out the tutorials on transforming, summarizing, visualizing and reshaping data in the “data wrangling with the tidyverse” category.

  3. a good understanding of descriptive statistics (distributions, parameters of central tendency) and frequentist inference (standard error, p-value, confidence intervals).

  4. some basic knowledge of regression models (e.g., linear regression, logistic regression, multilevel linear regression). In the practical part of the workshop, we will fit such models in R.

Note: The workshop will NOT provide an introduction to R!



Schedule

Monday, 10th October 2022

Time Topic
9:00 - 9:30: Welcome
9:30 - 10:30: An Introductory Example
10:30 - 12:00: Basics of Bayesian Statistics
12:00 - 13:00: R: Exercise I
13:00 - 14:00: Lunch Break
14:00 - 14:45: Markov Chain Monte Carlo
14:45 - 15:30: Bayesian Inference vs. NHST
15:30 - 16:00: Coffee Break
16:00 - 17:00: R: Exercise II


Tuesday, 11th October 2022

Time Topic
9:00 - 10:00: Subjective Beliefs and Knowledge Cumulation
10:00 - 10:30: Short recap of Day 1
10:30 - 11:30: Simple and Multiple Regression
11:30 - 13:00: R: Exercise III
13:00 - 14:00: Lunch Break
14:00 - 14:45: Multilevel Regression
14:45 - 15:30: R: Exercise IV
15:30 - 16:00: Coffee Break
16:00 - 17:00: Q&A


This course is published under the following license.