Ruprecht-Karls-Universit�t Heidelberg

Ulf Mertens, M.Sc.

Ulf Mertens

Ulf Mertens

Universität Heidelberg
Psychologisches Institut
Hauptstrasse 47-51
D-69117 Heidelberg

Room: F023

email: ulf(dot)mertens(at)
Tel: +49-6221-54-7322
Fax: +49-6221/54-7787

I don't have fixed office hours, so just write me a mail or call in order to make an appointment.

My personal blog:

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Research interests

  • Adaptive Design Optimization
    Adaptive Design Optimization (ADO) describes an algorithm that allows choosing for each trial of an experiment the stimulus or condition that is most informative in terms of model comparison. After each gathered datum, the ADO algorithm searches for the stimulus that results in the most pronounced differences among the different model’s predictions. Due to this optimization procedure, the number of trials needed to choose the true model can be reduced significantly.
    • Bayesian Statistics
    • The algorithm relies on the use of Bayesian Statistics. The parameters of the different models as well as the respective model probabilites are updated repeatedly via MCMC/Gibbs sampling.
    • Machine learning
    • ADO is closely related to Active Learning. In the context of machine learning, it is often the case that there is a lot of unlabeled data available (images, handwritten digits etc.). Since labeling of those data is a time-consuming and expensive task, simply choosing data points at random is an inefficient way. Active Learning however picks cases for which the current model (e.g linear classifier) has most uncertainty. This procedure reduces the required number of labeled cases to reach a certain accuracy.
  • Linear mixed models
  • Linear mixed models (LMMs) are a popular statistical tool to model hierarchical structures in the data. Especially in psychology, LMMs are often used for the analysis of repeated measurements. I am currently working on a comparison of some well-known methods for such designs including ANOVA, MANOVA and LMMs.


  • Übung Deskriptivstatistik
  • An introduction to basic descriptive statistics using the R programming language
  • Übung Inferenzstatistik
  • This is a subsequent course where the focus lies on how to use R for inferential statistics (linear models, contrast analysis etc.)
  • Statistics Toolbox
  • In this class, we show how to tackle some common problems in empirical theses such as dealing with violations of assumptions, missing values, marginally significant results and so on.
  • Data visualization with R
  • This class focuses on how to create publication-quality plots with both base R as well as the ggplot2-package.

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Seitenbearbeiter: Ulf Mertens