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
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.
Moen, Per August Jarval; Glad, Ingrid Kristine; Tveten, Martin. Efficient sparsity adaptive changepoint estimation. Electronic Journal of Statistics (ISSN 1935-7524). 18(2) pp 3975-4038. doi: 10.1214/24-EJS2294. 2024.
Manzanares-Salor, Benet; Sánchez, David; Lison, Pierre. Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack. Data mining and knowledge discovery (ISSN 1384-5810). pp 1-36. doi: 10.1007/s10618-024-01066-3. 2024.
Heinrich-Mertsching, Claudio Constantin; Thorarinsdottir, Thordis Linda; Guttorp, Peter; Schneider, Max. Validation of point process predictions with proper scoring rules. Scandinavian Journal of Statistics (ISSN 0303-6898). doi: 10.1111/sjos.12736. 2024.
Engebretsen, Solveig; Aldrin, Magne Tommy; Staven, Fredrik Ribsskog; Bendiksen, Eskil; Stige, Leif Christian; Jansen, Peder Andreas. Heterogeneous Weight Development of Lumpfish (Cyclopterus lumpus) Used as Cleaner Fish in Atlantic Salmon (Salmo salar) Farming. Fishes (ISSN 2410-3888). 9(9) doi: 10.3390/fishes9090336. 2024.