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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

    Tvete, Ingunn Fride; Bjørner, Trine; Skomedal, Tor. Mental Health and Disability Pension Onset Changes in Consumption of Antianxiety and Hypnotic Drugs. Health Services Research and Managerial Epidemiology (ISSN 2333-3928). 5 doi: 10.1177/2333392818792683. 2018.

    Kampffmeyer, Michael C.; Salberg, Arnt Børre; Jenssen, Robert. Urban land cover classification with missing data modalities using deep convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (ISSN 1939-1404). 11(6) pp 1758-1768. doi: 10.1109/JSTARS.2018.2834961. 2018.

    Lund, Eiliv; Nakamura, Aurelie; Snapkov, Igor; Thalabard, Jean-Christophe; Olsen, Karina Standahl; Holden, Lars; Holden, Marit. Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients. Clinical Epidemiology (ISSN 1179-1349). doi: 10.2147/CLEP.S163208. 2018.

    Guttorp, Peter; Thorarinsdottir, Thordis Linda. How to save Bergen from the sea? Decisions under uncertainty. Significance (ISSN 1740-9705). 15(2) pp 14-18. doi: 10.1111/j.1740-9713.2018.01125.x. 2018.

    Skeie, Ragnhild Bieltvedt; Berntsen, Terje Koren; Aldrin, Magne Tommy; Holden, Marit; Myhre, Gunnar. Climate sensitivity estimates - Sensitivity to radiative forcing time series and observational data. Earth System Dynamics (ISSN 2190-4979). 9(2) pp 879-894. doi: 10.5194/esd-9-879-2018. 2018.

Publications in 2018, 2017, 2016, 2015, 2014, earlier years
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
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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