Bayesian statistics

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Within this line of research, the use of Bayesian methods for data analysis is examined and new Bayesian techniques are being developed.

In addition to the data, Bayesian statistics also make use of so-called prior knowledge that is summarized in a prior distribution. This prior distribution makes explicit the expectations of the researcher before conducting the study. The use of priors is increasingly popular. Part of our research therefore focuses on examining how such a prior distribution can best be specified and how it can influence the results (desirable or sometimes undesirable).

We also develop new Bayesian methods, such as Bayes factors for informative hypotheses, elicitation methods, approximate measurement invariance, and Dynamic Structural Equation Modeling.