GESIS Training Courses
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Scientific Coordination

André Ernst
Tel: +49 221 4703736

Administrative Coordination

Janina Götsche

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Introduction to Bayesian Statistics

About
Location:
Online via Zoom
 
Course duration
We+Th: 9:00-15:15 CEST
Fr: 9:00-12:15 CEST
 
General Topics:
Data Analysis
Course Level:
Advanced
Format:
Workshop
Software used:
R
Duration:
3 days
Language:
English
Fees:
Students: 200 €
Academics: 300 €
Commercial: 600 €
 
Keywords
Additional links
Lecturer(s): Denis Cohen

About the lecturer - Denis Cohen

Course description

Bayesian methods for inference and prediction have become widespread in the social sciences (and beyond). Over the last decades, applied Bayesian modeling has evolved from a niche methodology with high computational and software-specific entry barriers to a readily available toolbox that virtually everyone can use by running pre-implemented packages in standard statistical software on generic PCs. Although Bayesian methods are now more accessible than ever before, aspiring Bayesian practitioners may be overwhelmed by questions and choices - including, but not limited to, when and why to use Bayesian methods in applied research, how to implement and interpret Bayesian analyses, or which software to use.
 
This workshop is designed to help participants take these first steps. It juxtaposes frequentist and Bayesian approaches to estimation and inference, highlights the distinct characteristics and advantages of Bayesian methods, and introduces participants to the Bayesian workflow and applied modeling using the R package brms - an accessible interface to the probabilistic programming language Stan, which allows users to perform Bayesian inference with state-of-the-art algorithms by running little more than a few lines of code in R.


Target group

Participants will find the course useful if:
  • They have had prior (theoretical) exposure to frequentist statistics and always wondered about the what, why, and how of using Bayesian statistics.
  • They have previously used frequentist statistical models for applied research and now seek to go Bayesian.
  • They want to get a hands-on introduction to applied Bayesian modeling using pre-implemented software packages in R.


    • Learning objectives

    By the end of the course participants will:
  • Know fundamental concepts in Bayesian statistics and be able to contrast them with frequentist approaches.
  • Know how to implement the workflow for applied Bayesian statistical modeling using the pre-implemented R package brms.
  • Have freshened up and expanded their understanding of statistical inference and statistical models.
    •  
     
    Organizational structure of the course
    This course is organized into eight sessions à 90 minutes, with three sessions on the first two workshop days and two sessions on the third.
     
    The first two workshop days combine two 90-minute morning lectures with a 90-minute afternoon lab. A 90-minute lunch break divides morning and afternoon sessions:
  • Lectures will alternate between classical lecturing, interactive Q&A sessions, and regular applied code demonstrations and empirical illustrations.
  • Labs allow participants to work collaboratively on applied problem sets in small groups in Zoom breakout rooms. The problem sets typically comprise a sequence of problems as typically encountered in real-world empirical work. Hints will be provided, and the instructor will be available in the main meeting room to assist with any questions that may arise. Toward the end of the session, we will jointly discuss the solutions to the problem sets.
    •  
    The third day features two morning sessions:
  • The first session (120 min) allows participants to apply their newly acquired knowledge in implementing small individual data projects using Bayesian methods. Participants can work on their projects and discuss any ideas and challenges with the instructor.
  • The second session (60 min) will be dedicated to jointly discussing your experiences of implementing your first Bayesian data-analytical project and providing an outlook on how you can move forward and grow as a Bayesian practitioner. We conclude with course evaluations and feedback.


    • Prerequisites

  • Working knowledge of the software environment R
  • Working knowledge of (generalized) linear models
  • Ideally, you have a small data project that you would like to implement using Bayesian methods
    •  
    Software requirements
    This workshop requires installations of recent versions of R, RStudio, as well as the packages rstan, brms, and marginaleffects. Setting up rstan can be somewhat time-consuming as it requires the installation of a free-of-charge C++ compiler. Before the workshop, participants should follow these instructions on the Stan Development Team's GitHub to install and configure the rstan package and its prerequisites on their operating system. If you do not have administrator privileges on your machine, please approach your system administrator in advance of the workshop.
     
     
    Agenda
     
    Wednesday, 22.11.
    09:00-12:15
  • Introduction
  • Refresher on R and frequentist inference
  • Limitations of frequentist inference
    •  
  • Fundamental concepts in Bayesian inference
  • Analytical Bayes
  • Numerical Bayes via Markov-Chain Monte Carlo sampling
    • 12:15-13:45Lunch Break
    13:45-15:15
  • Group exercises
  • Solutions and discussion
    • Thursday, 23.11.
    09:00-12:15
  • Going Bayesian in applied research: When, why, and how?
  • Software solutions: An overview
  • The Bayesian workflow
    •  
  • The brms package: Functionality
  • Doing Bayesian data analysis with brms: A step-by-step walkthrough
    • 12:15-13:45Lunch Break
    13:45-15:15
  • Group exercises
  • Solutions and discussion
    • Friday, 24.11.
    09:00-12:15
  • Implementation of individual data projects using brms
  • Alternatively: Implementation of a small data project provided by the instructor
  • Assistance by and individual consultations with the instructor
    •  
  • Review of challenges and potentials encountered in working on your data projects
  • Outlook: Moving forward as Bayesian practitioners
  • Course evaluations and feedback


    • Recommended readings