The statistics section conducts teaching within a wide variety of topics in statistics and applied probability. The research is mainly focused on spatial statistics (particularly spatial point processes), stochastic geometry, forensic statistics and computational statistics.

Point processes play an important role in spatials statistics as models for patterns of locations of objects randomly dispersed in space. On a large scale, such objects may be trees in a rain forest while on a micro-scale objects may represent cells in the brain or protein molecules within a cell. Many important biological hypotheses are concerned with the nature of the spatial patterns - e.g. whether they are clustered or regular. Stochastic geometry is concerned with probabilistic aspects of random geometric objects and random tesselations.

Forensic genetics and DNA-investigations are an increasingly important part of e.g. immigration-, paternity- and criminal cases. The question of quantification and presentation of the weight of the DNA evidence for the court contains many statistical and philosophical statistical problems and research in forensic statistics further has to acknowledge the special world of decision-making in the courtroom.

Computational statistics is a broad term for statistical methodology that involve intensive computations or where key focus is on implementation on a computer. Markov chain Monte Carlo, for example, is a very useful tool in spatial statistics and in Bayesian approaches within forensic statistics. Development of open-source statistical software such as R is another important topic in computational statistics.




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