We conduct research on Bayesian inference, developing its methodological, computational, and applied aspects.
Special topics of interest are: Monte Carlo methods including Markov chain Monte Carlo and sequential Monte Carlo (particle filters); likelihood-free methods for models with intractable likelihoods (such as approximate Bayesian computation aka ABC); the role of improper priors; scalable methods for large spatio-temporal models.
Application areas include: modelling of protein folding data; tumour growth in mice; modelling of virus capsid assembly, forensic statistics, stochastic models for shape deformations, FitzHugh-Nagumo modelling in neuro science, ice core data.
Seminars
We have regularly speakers at the Statistics Seminar.
Group members
Senior members
- Full Professor, Applied Mathematics and Statistics, Mathematical Sciences
- Full Professor, Applied Mathematics and Statistics, Mathematical Sciences
- Professor, Applied Mathematics and Statistics, Mathematical Sciences
- Studierektor, Applied Mathematics and Statistics, Mathematical Sciences
- Senior Lecturer, Applied Mathematics and Statistics, Mathematical Sciences
PhD Students
Ongoing projects
2020-01-01–2023-12-31 VR and Chalmers AI Research council
Deep learning and likelihood-free Bayesian inference for stochastic modelling
Project leader: Umberto Picchini
2020-01-01–2023-12-31 Chalmers AI Research council
Stochastic continuous-depth neural networks
Project leader: Moritz Schauer
Past projects
2017-01-01–2020-12-31 VR
Latent jump fields for spatial statistics
Project leader: David Bolin
2014-01-01–31-12-2019 VR
Statistical inference and stochastic modelling of protein folding
Project leader Umberto Picchini
2012-01-01–2017-12-31 VR
Improving Bayesian Network computational and modeling methods, with applications in forensics.
Project leader Petter Mostad
