Projects

The Bayesian Graphical Modeling Lab consists of an enthusiastic group of researchers who are constantly developing new graphical models and novel methods for analyzing them. This provides an ideal environment for students to hone their statistical and methodological skills through internships or theses. Student projects typically contribute to an ongoing project in the lab and typically involve programming new methods, performing analyses, and performing quality checks on new methods.

Below is an overview of the research areas in which we typically have projects available in our lab. Please note that these are general categories, not specific projects. If you are interested in a research project in one of these areas, please contact one of the associated researchers!

• Develop graphical models of psychological data or analyze their properties (e.g., their dynamics). Jonas Haslbeck, Maarten Marsman, & Lourens Waldorp.

• Implement or evaluate Markov chain Monte Carlo or computational methods to analyze graphical models. Giuseppe Arena, Maarten Marsman, & Don van den Bergh

• Bayesian methods for analyzing graphical cross-sectional models, including prior specification, derivation, implementation, or evaluation of Bayesian hypothesis tests. Nikola Sekulovski, & Maarten Marsman

• Bayesian methods for analyzing graphical longitudinal models, including prior specification, derivation, implementation, or evaluation of Bayesian hypothesis tests. Jonas Haslbeck, & Maarten Marsman

• Projects that demonstrate the Bayesian approach to graphical modeling, i.e. create demos or vignettes, but also tutorials and guidelines. Nikola Sekulovski, & Johnny van Doorn

• Re-analyze existing data using Bayesian graphical models. Jonas Haslbeck, & Nikola Sekulovski