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
Guttorp, Peter; Särkkä, Aila; Ylitalo, Anna-Kaisa. What we look at in paintings: A comparison between experienced and inexperienced art viewers. Annals of Applied Statistics (ISSN 1932-6157). 10(2) pp 549-574. doi: 10.1214/16-AOAS921. 2016.
Pettersen, Jostein Mulder; Brynildsrud, Ola Brønstad; Huseby, Ragnar Bang; Rich, Karl; Aunsmo, Arnfinn; Jensen, Britt Bang; Aldrin, Magne Tommy. The epidemiological and economic effects from systematic depopulation of Norwegian marine salmon farms infected with pancreas disease virus. Preventive Veterinary Medicine (ISSN 0167-5877). 132 pp 113-124. doi: 10.1016/j.prevetmed.2016.09.001. 2016.
Lillenes, Meryl Sønderby; Rabano, Alberto; Støen, Mari; Riaz, Tahira; Misaghian, Dorna; Møllersen, Linda; Esbensen, Qin Ying; Günther, Clara-Cecilie; Selnes, Per; Stenset, Vidar; Fladby, Tormod; Tonjum, Tone. Altered DNA base excision repair profile in brain tissue and blood in Alzheimer’s disease. Molecular Brain (ISSN 1756-6606). 9:61 doi: 10.1186/s13041-016-0237-z. 2016.
Wold, Jens Petter; Kermit, Martin Andreas; Segtnan, Vegard. Chemical Imaging of Heterogeneous Muscle Foods Using Near-Infrared Hyperspectral Imaging in Transmission Mode. Applied Spectroscopy (ISSN 0003-7028). 70(6) pp 953-961. doi: 10.1177/0003702816641260. 2016.
Guttorp, Peter; Ylitalo, Anna-Kaisa; Särkkä, Aila. What we look at in paintings: A comparison between experienced and non-experienced art viewers. Annals of Applied Statistics (ISSN 1932-6157). 10(2) pp 549-574. doi: 10.1214/16-AOAS921. 2016.
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