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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

    Choi, Changkyu; Kampffmeyer, Michael; Jenssen, Robert; Handegard, Nils Olav; Salberg, Arnt-Børre. Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data. IEEE Journal of Oceanic Engineering (ISSN 0364-9059). doi: 10.1109/JOE.2022.3226214. 2023.

    Tvete, Ingunn Fride; Aldrin, Magne Tommy; Jensen, Britt Bang. Towards better survival: Modeling drivers for daily mortality in Norwegian Atlantic salmon farming. Preventive Veterinary Medicine (ISSN 0167-5877). 210 doi: doi.org/10.1016/j.prevetmed.2022.105798. 2023.

    Bellier, Joseph; Whitin, Brett; Scheuerer, Michael; Brown, James; Hamill, Thomas M.. A multi-temporal-scale modulation mechanism for the post-processing of precipitation ensemble forecasts: Benefits for streamflow forecasting. Journal of Hydrometeorology (ISSN 1525-755X). doi: 10.1175/JHM-D-22-0119.1. 2023.

    Switanek, Matthew B.; Hamill, Thomas M.; Long, Lindsey N.; Scheuerer, Michael. Predicting subseasonal tropical cyclone activity using NOAA and ECMWF reforecasts. Weather and forecasting (ISSN 0882-8156). doi: 10.1175/WAF-D-22-0124.1. 2022.

    Vitelli, Valeria; Fleischer, Thomas; Ankill, Jørgen; Arjas, Elja; Frigessi, Arnoldo; Kristensen, Vessela N.; Zucknick, Manuela. Transcriptomic pan-cancer analysis using rank-based Bayesian inference. Molecular Oncology (ISSN 1574-7891). doi: 10.1002/1878-0261.13354. 2022.

Publications in 2023, 2022, 2021, 2020, 2019, earlier years
Postal address:
Norsk Regnesentral/
Norwegian Computing Center
P.O. Box 114 Blindern
NO-0314 Oslo
Visit address:
Norsk Regnesentral
Gaustadalleen 23a
Kristen Nygaards hus
NO-0373 Oslo.
(+47) 22 85 25 00
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Postal address: Norsk Regnesentral/Norwegian Computing Center, P.O. Box 114 Blindern, NO-0314 Oslo, Norway
Visit address: Norsk Regnesentral, Gaustadalleen 23a, Kristen Nygaards hus, NO-0373 Oslo.
Phone: (+47) 22 85 25 00
AddressHow to get to NR