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Alisa Remizova
Tel: +49 221 47694-475

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Noemi Hartung
Tel: +49 621 1246-211

Advanced Bayesian Statistical Modeling in R and Stan

About
Location:
Online via Zoom
 
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
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Lecturer(s): Denis Cohen

About the lecturer - Denis Cohen

Course description

Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks.
 
This workshop equips participants with the required skills and tools for their first attempts at custom Bayesian statistical modeling. Rather than running through a series of statistical models, it teaches the mechanics of hands-on statistical modeling: Following an advanced theoretical and applied recap of the building blocks of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises allow participants to write and run various model types and to process the resulting estimates into publication-ready graphs.
 


Target group

Participants will find the course particularly useful if:
  • They seek an advanced introduction to applied Bayesian statistical modeling and model building
  • They have used statistical models but want to understand the underlying mechanics of estimation, inference, and the post-estimation processing into meaningful quantities of interest
  • They want to move beyond using pre-implemented models and craft custom solutions that meet the idiosyncratic requirements of specific empirical applications


Learning objectives

By the end of the course participants will:
  • Know Bayesian fundamentals and know how to implement the workflow for Bayesian statistical model building
  • Know how to translate formal statistical models into code using Stan to start crafting their own custom models
  • Know how to process estimates from generalized linear models into substantively meaningful quantities of interest (without relying on pre-implemented software)
  • Know how to use distributional summaries as a flexible framework for reporting inferential uncertainty


Prerequisites

  • Intermediate working knowledge of the software environment R
  • Intermediate working knowledge of (generalized) linear models
  • Basic to intermediate knowledge of linear algebra (recommended)
  • Basic to intermediate knowledge of probability theory (recommended)
  •  
    Software requirements
    This workshop requires installations of recent versions of R, RStudio, and RStan. Throughout the second half of the workshop, we will use Stan via its R interface RStan. Setting up RStan can be somewhat time-consuming as it requires the installation of a free-of-charge C++ compiler. Therefore, 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 before the workshop. If you do not have administrator privileges on your machine, please approach your system administrator in advance of the workshop.


    Schedule