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
We use statistical methods to quantify all kinds of risk in the banking, insurance and commodity markets. We work a lot with models related to Basel II and Solvency II and have developed a large number of systems related to trading and risk management for various power companies.
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We cover a wide range of applications within the fields of climate, environment, marine resources and health. To mention a few, we work with climate sensitivity estimation, reconstructing past global climate, pollution in air and water, abundance estimation of wild fish and whales, and we also do research in genomics and bioinformatics.
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We cover a wide range of applications including forecasting, time series analysis, network modelling, optimal delivery, logistics, optimal steering of industry processes, reliability analysis, cost surveys and analysis of very large data sets.
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We do research and development of methodologies for analysis of digital remote sensing data from satellites and aircrafts. Application areas include environmental monitoring, climate change monitoring, environmental emergency detection, natural resource mapping and map revision.
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We have extensive knowledge in the fields of image analysis and machine learning. Object extraction, characterization and recognition from images are central for many of these applications. Another central theme is the analysis of sequences of data, including motion detection and characterization.
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This center for research-based-innovation is funded by the Research Council of Norway. Big Insight produces innovative solutions for key challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.
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Last 5 scientific articles
Aldrin, Magne Tommy; Huseby, Ragnar Bang; Stien, Audun; Grøntvedt, Randi Nygaard; Viljugrein, Hildegunn; Jansen, Peder A. A stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms – Estimated from multiple farm data sets. Ecological Modelling (ISSN 0304-3800). 359 pp 333-348. doi: 10.1016/j.ecolmodel.2017.05.019. 2017.
Gjerden, Pål; Bramness, Jørgen Gustav; Tvete, Ingunn Fride; Slørdal, Lars. The antipsychotic agent quetiapine is increasingly not used as such: dispensed prescriptions in Norway 2004–2015. European Journal of Clinical Pharmacology (ISSN 0031-6970). pp 1-7. doi: 10.1007/s00228-017-2281-8. 2017.
Salberg, Arnt Børre; Trier, Øivind Due; Kampffmeyer, Michael C.. Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks. In: Image Analysis 20th Scandinavian Conference, SCIA 2017 Tromsø, Norway, June 12–14, 2017 Proceedings, Part II. (ISBN 978-3-319-59128-5). pp 193-204. doi: 10.1007%2F978-3-319-59129-2_17. 2017.
Aas, Kjersti; Neef, Linda Reiersølmoen; Williams, Lloyd; Raabe, Dag. Interest rate model comparisons for participating products under Solvency II. Scandinavian Actuarial Journal (ISSN 0346-1238). doi: 10.1080/03461238.2017.1332679. 2017.
Kutuzov, Andrei; Lison, Pierre. Redefining Context Windows for Word Embedding Models: An Experimental Study. In: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. (ISBN 978-91-7685-601-7). pp 284-288. 2017. Full-text
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