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
Our research within statistics for climate and environment is a mixture of applied, problem-driven projects and fundamental research eligible for publication in scientific journals.
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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 areas marine resources and health.
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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
Aastveit, Marthe Elisabeth; Cunen, Celine Marie Løken; Hjort, Nils Lid. A new framework for semi-Markovian parametric multi-state models with interval censoring. Statistical Methods in Medical Research (ISSN 0962-2802). doi: 10.1177/09622802231160550. 2023.
Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda; Xu, Chong-Yu. Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach. Journal of Hydrology (ISSN 0022-1694). 620 doi: 10.1016/j.jhydrol.2023.129448. 2023.
Yuan, Qifen; Thorarinsdottir, Thordis; Beldring, Stein; Wong, Wai Kwok; Xu, Chong-Yu. Assessing uncertainty in hydrological projections arising from local-scale internal variability of climate. Journal of Hydrology (ISSN 0022-1694). doi: 10.1016/j.jhydrol.2023.129415. 2023.
Howell, Daniel; Godiksen, Jane Aanestad; Tranang, Caroline Aas; Berg, Erik; Bernreuther, Matthias; Bogstad, Bjarte; Breivik, Olav Nikolai; Casas, Jose Miguel; Clain, Laura; Eidset, Elise; Eriksen, Elena; Fall, Johanna Jennifer Elisabeth; Fossheim, Maria; Gjøsæter, Harald; Gundersen, Sofie; Hallfredsson, Elvar Halldor; Höffle, Hannes; Johannessen, Edda; Nedreaas, Kjell Harald; Nielsen, Anders; Skaret, Georg; Staby, Arved; Stock, Brian; Subbey, Samuel; Tallman, Ross; Trochta, John Tyler; Vollen, Tone; Windsland, Kristin. Arctic Fisheries Working Group (AFWG; outputs from 2022 meeting). ICES Scientific Reports (ISSN 2618-1371). 5(18) pp 1-507. doi: 10.17895/ices.pub.20012675. 2023.
Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders. Some recent trends in embeddings of time series and dynamic networks. Journal of Time Series Analysis (ISSN 0143-9782). doi: 10.1111/jtsa.12677. 2023.
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