Department: Methodology & Statistics

Organisation: Utrecht University

Country: The Netherlands



Milica received her PhD in Quantitative Psychology at Arizona State University. Her research interests center around evaluating the benefits and risks of using Bayesian methods for mediation analysis.

Research topic

In her current projects, Milica is focusing on optimal methods for data synthesis from non-exchangeable studies, on the consequences of specifying inaccurate priors in mediation models, and on issues that arise in applications of Bayesian mediation analysis with informative prior distributions in small samples.


The research on the risks of inaccurate priors in Bayesian analysis and on optimal ways to specify priors from non-exchangeable sources is partly funded by the Vidi Grant.

Projects involved

Small Samples

Researchers often have difficulties collecting enough data to obtain statistical power: when target groups are small (e.g., children with severe burn injuries), hard to access (e.g., infants of drug-dependent mothers), or measuring the participants requires prohibitive costs (e.g., measuring phonological difficulties of babies). Such obstacles to collecting data usually leads to a limited data set. Researchers can overcome this through simplifying their hypotheses and statistical models. However, this strategy is undesirable since the intended research question cannot be answered in this way.

My First Bayes

Since the beginning of the 21st century, Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research.

Expert Data (Dis)agreement

Elicitation is the process of extracting knowledge about the parameters in the statistical model. This information can then be used to provide input for the prior distribution needed for Bayesian analysis. Several methods of prior elicitation are used in practice including the use of experts.