SAMBA
SAMBA
Statistical Analysis, Machine Learning and Image Analysis - SAMBA
The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.
Research areas
Last 5 scientific articles
Vandeskog, Silius Mortensønn; Huser, Raphaël; Bruland, Oddbjørn; Martino, Sara. Fast spatial simulation of extreme high-resolution radar precipitation data using integrated nested Laplace approximations. The Journal of the Royal Statistical Society, Series C (Applied Statistics) (ISSN 0035-9254). doi: 10.1093/jrsssc/qlae074. 2024.
Skeie, Ragnhild Bieltvedt; Aldrin, Magne Tommy; Berntsen, Terje Koren; Holden, Marit; Huseby, Ragnar Bang; Myhre, Gunnar; Storelvmo, Trude. The aerosol pathway is crucial for observationally constraining climate sensitivity and anthropogenic forcing. Earth System Dynamics (ESD) (ISSN 2190-4979). 15(6) pp 1435-1458. doi: 10.5194/esd-15-1435-2024. 2024.
Pilán, Ildikó; Prévot, Laurent; Buschmeier, Hendrik; Lison, Pierre. Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis. In: Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue. (ISBN 979-8-89176-161-2). pp 440-457. 2024.
Schneider, Max; Guttorp, Peter. What Do We Know Without the Catalog? Eliciting Prior Beliefs from Experts for Aftershock Models. The Seismic Record (TSR) 4(4) pp 259-267. doi: 10.1785/0320240008. 2024.
Worsnop, Rochelle P.; Scheuerer, Michael; Hamill, Thomas M.; Smith, Timothy A.; Schlör, Jakob. RUFCO: a deep-learning framework to post-process subseasonal precipitation accumulation forecasts. Artificial Intelligence for the Earth Systems (ISSN 2769-7525). doi: 10.1175/AIES-D-24-0020.1. 2024.