Interactive Visual Assessment of Bayesian models

Theoretical (Analytical):

Practical (Implementation):

Literature Work:

Overview and Background

Bayesian statistical models can be used to analyze data sets. Compared to the frequentist approach they include some interesting features like the explicit statement of priors. As any statistical model they are based on several assumptions that are not trivial to be checked automatically. Statisticians often use visualizations to help them inspecting their models. However, they usually use unconnected static visualizations. 

Problem Statement

The main question to be elaborated in this project is whether an interactive visual analytics approach to some class of Bayesian models can add additional value beyond the state-of-the-art approach. The goal is to compose an integrated application that guides analyst through the workflow, shows suitable visualizations when needed, helps in specifying parameters and aids in assessing the estimation process
as well as its results. Ideally, the system automatically points out problems and suggests potential solutions. To begin with, the system shall be focused on a particular problem set and show that a visual analytics approach can help analysts do their work. 


  • Identify workflow and relevant information at each stage 
  • Find suitable visualizations from previous work 
  • Develop a visual analytic prototype for building and assessing one
    class of Bayesian statistical models 
  • Evaluate your approach 


  • Basic/Advanced knowledge of Bayesian (regression) models 
  • Good programming skills 


  • Scope: Master
  • 6 Months Project, 6 Months Thesis 



  • Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2018). Visualization in Bayesian workflow
  • Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin (2013). Bayesian Data Analysis (Third ed.). Chapman & Hall/CRC.