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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

    Basse-O'Connor, Andreas; Heinrich, Claudio Constantin; Podolskij, Mark. On limit theory for functionals of stationary increments Lévy driven moving averages. Electronic Journal of Probability (ISSN 1083-6489). 24(79) pp 1-42. doi: 10.1214/19-EJP336. 2019.

    Jang, Youngsoo; Lee, Jongmin; Park, Jaeyoung; Lee, Kyeng-Hun; Lison, Pierre; Kee-Eung, Kim. PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), System Demonstrations. (ISBN 9781950737925). pp 187-192. doi: 10.18653/v1/D19-3032. 2019.

    Fredricson Marquez, Jonatan; Lee, Aline Magdalena; Aanes, Sondre; Engen, Steinar; Herfindal, Ivar; Salthaug, Are; Sæther, Bernt-Erik. Spatial scaling of population synchrony in marine fish depends on their life history. 2019.

    Bianchi, Filippo Maria; Livi, Lorenzo; Mikalsen, Karl Øyvind; Kampffmeyer, Michael C.; Jenssen, Robert. Learning representations of multivariate time series with missing data. Pattern Recognition (ISSN 0031-3203). 96:106973 pp 1-11. doi: 10.1016/j.patcog.2019.106973. 2019.

    Reksten, Jarle Hamar; Salberg, Arnt Børre; Solberg, Rune. Flood detection in Norway based on Sentinel-1 SAR imagery. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (ISSN 1682-1750). XLII-3/W8 pp 349-355. doi: 10.5194/isprs-archives-XLII-3-W8-349-2019. 2019.

Publications in 2019, 2018, 2017, 2016, 2015, 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