Computational methods are an essential link connecting data to statistical models and learning. Our group has diverse expertise in producing methods for large or complex datasets, as well as in their performance analysis.  Some key interests include:

• Monte Carlo methods
• Bayesian methods and their approximation
• Methods for network-valued or heavy-tailed data
• Emulation of complex statistical models
• Applications in engineering and biology

The pages accessible via the top bar contain information of our group members, as well as any upcoming news, events, or other points of interest. If you’d like to contribute an item, or want to get in touch with the group, contact Dr Jere Koskela.