Stochastic Models and Inference
Innovative computationally intensive inference for complex stochastic models in the life sciences. We develop statistical methodology motivated by specific problems in science, technology, industry and society.
About the group
In our group we develop innovative stochastic models that try to carefully represent fundamental principles, basic dynamics, intricate patterns of dependence and known mechanisms of the system to be understood or predicted. In this way, models can better answer specific questions, qualitative or quantitative in nature, and posses more power. Inference is usually based on computationally intensive stochastic algorithms, like MCMC.
We prefer the Bayesian approach, but many projects are likelihood based. Currently, our collaborations are in cancer genomics, psychiatry, veterinary, infectious diseases, but also in finance, insurance, climate research.
Our group is also part of the Norwegian Centre for Research Based Innovation BigInsight, which is directed by Arnoldo Frigessi.