Universiteit Leiden

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Leiden University Center for Statistical Science


The members of LUXs develop and validate new methods and algorithms for analysing data, and apply these in a wide range of problems. Their research ranges from multidisciplinary applications to proving mathematical theorems.

Research projects

The research is too varied to be listed completely here, but some examples may set the idea.

See the pages of the members of LUXs for more detailed information.

Bayesian statistics

The Bayesian statistical method assigns probabilities to the free parameters describing the phenomenon under investigation, reflecting prior uncertainty, and next updates these using the available data. This leads to a quantitative method to express both our knowledge and our measure of certainty in this knowledge. The way of thinking is as simple as it is attractive, but 15 years ago its validity when applied to modern high-dimensional data or infinite-dimensional models was still completely unclear. Members of LUX are at the forefront of clarifying aspects of the Bayesian method and its many variants, and are also engaged in applications of such methods to e.g. genomical data.

Biomedical data science

The research of the section Medical Statistics focuses on the development, interpretation, evaluation and implementation of statistical methods and data management for (bio)medical research. The section participates in clinical research projects from both within the LUMC, as well as projects from other institutes. Research topics predominantly originate from practical problems encountered in research projects of medical researchers from the LUMC.

PhD students, post-docs and staff members work on statistical problems in survival analysis and multi-state modelling; diagnostics, and prognostic modelling; bioinformatics; statistics for genetics; longitudinal modelling; Bayesian inference; quality indicators; and causal modelling. Staff members cooperate with many departments of the LUMC on the design and implementation of medical studies, on data analysis, and on interpretation, critical review and publication of the results. Staff members also participate in (inter)national multi-centre projects and advise other research institutes.

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