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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

    Binde, Caroline Ditlev; Tvete, Ingunn Fride; Gåsemyr, Jørund Inge; Natvig, Bent; Klemp, Marianne. Comparative effectiveness of dopamine agonists and monoamine oxidase type-B inhibitors for Parkinson’s disease: a multiple treatment comparison meta-analysis. European Journal of Clinical Pharmacology (ISSN 0031-6970). 76(12) pp 1731-1743. 2020.

    Alfter, David; Volodina, Elena; Pilán, Ildikó; Lange, Herbert; Borin, Lars (eds). Proceedings of the 9th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2020). Linköping University Electronic Press. (ISBN 978-91-7929-732-9). pp 45. 2020.

    Pilán, Ildikó; Brekke, Pål H.; Dahl, Fredrik Andreas; Gundersen, Tore; Husby, Haldor; Nytrø, Øystein; Øvrelid, Lilja. Classification of Syncope Cases in Norwegian Medical Records. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop. (ISBN 978-1-952148-74-3). pp 79-84. 2020.

    Heinrich, Claudio Constantin. On the number of bins in a rank histogram. Quarterly Journal of the Royal Meteorological Society (ISSN 0035-9009). doi: 10.1002/qj.3932. 2020.

    Gilbert, Andrew David; Holden, Marit; Eikvil, Line; Rakhmail, Mariia; Babic, Aleksandar; Aase, Svein Arne; Samset, Eigil; Mcleod, Kristin. User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing. IEEE journal of biomedical and health informatics (ISSN 2168-2194). doi: 10.1109/JBHI.2020.3029392. 2020. Institutional archive 

Publications in 2020, 2019, 2018, 2017, 2016, 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