Bayesian Statistics group

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.


We have regularly speakers at the Statistics Seminar

Group members

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​

Mathematical Science is a joint department between Chalmers and the University of Gothenburg.