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Probability and Statistics

The field of probability and statistics deals with modeling phenomena in the presence of uncertainty. Our research in this area encompasses the full spectrum from theoretical to applied.

In probability theory, we work in several different areas, like random walks, queueing systems, networks, and stochastic processes. Another main focus is forensic probability and statistics. The evaluation of the evidential value of, say, (partial) DNA matches is not straightforward, with many practical, philosophical, and theoretical questions. Our activities range from (very) theoretical to genuinely applied, where we cooperate with many other disciplines, including physics, biology, law, and philosophy, including the philosophy of probability and statistics themselves.

In mathematical statistics the focus is on deriving theoretical guarantees for statistical procedures and developing efficient computational methods. Specific topics studied within the group include statistical inference for stochastic processes, nonparametric Bayesian inference, high-dimensional inference, quantile regression, hypothesis testing and Bayesian computation. In applied statistics the focus is on development, assessment and application of statistical models and methods for complex data structure. The main, sometimes overlapping, themes are modelling and inference for biological networks, high-dimensional molecular data, and neuroscience. Important aspects of this research are dependency, heterogeneity, integration of different data types and big data.

Sample publication

Sample publication

Safe Testing. / Peter Grünwald, Rianne de Heide, Wouter Koolen

Forthcoming as a discussion paper in the Journal of the Royal Statistical Society Series B. 

link to the article

Researchers and their interests

  • Eduard Belitser. High-dimensional inference, Bayesian nonparametrics.

    Area of expertise includes uncertainty quantification and structure recovery in high-dimensional models, Bayesian nonparametrics, stochastic approximation algorithms and nonparametric estimation.

  • Mathisca de Gunst . Statistics for Life Sciences.

    My research is in the area of stochastic modeling and statistical analysis of biological processes, and comprises development, assessment and application of statistical models and tools. I use mathematical models ranging from (non)linear regression, Markov and hidden Markov models to non-Markovian counting processes, and frequentist as well as Bayesian estimation techniques. My main interest is in statistical models for networks with a special focus on neuroscience applications.

  • Rianne de Heide. Hypothesis testing, Bayesian learning and bandit problems.

    I work on problems and solutions in machine learning and statistics. My research focuses on sequential learning, and in particular on (sequential) hypothesis testing, Bayesian learning and best-arm identification problems. I am interested in exploring the limitations of existing methods under misspecification of the model or the data collecting process, and in devising new, robust methods for these settings. I’m also interested in the mathematical and philosophical foundations of Bayesianism, machine learning, statistics and probability theory. 


  • Rikkert Hindriks. Statistical inference for EEG/MEG and fMRI.

    My expertise is in statistical inference, computational and inverse modeling of hemodynamic and electrophysiological brain signals, with particular focus on functional connectivity. My current interests include inference for non-Gaussian and higher-order functional connectivity and classification of invariant connectivity measures.

    Google scholar

  • Wouter Kager. Discrete Random Systems.

    I study discrete systems with random spatial behavior, such as lattice aggregation models, random fields and random walks. I am particularly interested in the limiting behavior and scaling limits of these models, although my work also concerns other topics in probability theory.

  • Ronald Meester. Theoretical and Applied Probability and Statistics.

    My research focusses on forensic probability and statistics, and on modelling and the application of probability theory and statistics in various disciplines: law, biology, physics, epidemiology and ecology. I am also interested in belief functions and in the philosophy of probability and statistics.

    Key publication: Probability and Forensic Evidence (

  • Frank van der Meulen. Statistical inference for stochastic processes.

    My research is directed to statistical inference for stochastic processes, with focus on uncertainty quantification and indirect observation schemes. I work on Bayesian computational aspects of inference for discretely observed stochastic processes on graphical models with particular emphasis on diffusion- and Lévy processes. More generally, I am interested in Bayesian computational methods such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC). On a somewhat more theoretical level I am interested in proving posterior contraction rates for Bayesian procedures, for example in censoring or shape restricted nonparametric problems. Finally an important aspect of my research consists of direct collaboration with researchers in fields outside mathematics. Examples include sports engineering (analysis of multilevel longitudinal data), climate projections, and maritime engineering. 


  • Paulo Andrade Serra. Mathematical Statistics.

    I have a wide range of research interests within (non-parametric) Mathematical Statistics including spline estimators, Bayesian non-parametrics and its frequentist properties, empirical Bayes, adaptation, statistical tracking and stochastic optimisation, non-parametric regression, quantile regression, and multi-stage estimation procedures. Although I focus mostly on theory, I am also very interested in implementation of algorithms and their practical applications, and on-line (recursive) algorithms.

  • Klaas Slooten. Forensic Probability and Statistics.

    My research focusses on probabilistic and statistical aspects of kinship analysis with DNA. In particular I am interested in the understanding of likelihood ratio methods.

    Key publication: Probability and Forensic Evidence (

  • Wessel van Wieringen. High-dimensional data and regularized learning.

    My primary interest is in modeling data stemming from complex phenomena that have been characterized in xof the model's parameters from the high-dimensional data, how this learning may benefit from existing knowledge present in related domains, and inference regarding this parameter within this setting.