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.

We have also a Bayesian reading group.

## Senior members:

David Bolin (Bayesian methods for spatial and spatio-temporal models)

Olle Häggström (fundamentals of Bayesian reasoning, applications in futurology, Markov chain Monte Carlo)

Rebecka Jörnsten (data integration and network modeling for multi-omics single cell and bulk data)

Petter Mostad (Bayesian forensic statistics, theory for improper priors)

Umberto Picchini (likelihood-free inference, ABC, inference for SDEs, general Monte Carlo methods)

Moritz Schauer (Bayesian inference for SDEs and Markov processes, data assimilation, Nonparametric Bayes)

## Current PhD students:

Anton JohanssonPetar Jovanovski

## Ongoing projects:

Deep learning and likelihood-free Bayesian inference for stochastic modelling

(Project leader: Umberto Picchini)

- 2020-01-01--2023-12-31 Chalmers AI Research council

(Project leader: Moritz Schauer)

### Past projects:

Latent jump fields for spatial statistics

(Project leader: David Bolin)

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)