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

Statistics for finance, insurance and commodity markets

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.

Statistics for climate, environment, marine resources and health

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.

Statistics for technology, industry and administration

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.

Earth observation

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.

Image Analysis and Machine Learning

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.

Big Insight

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.

Last 5 scientific articles

Deilkås, Ellen C Tveter; Risberg, Madeleine Borgstedt; Haugen, Marion; Lindstrøm, Jonas Christoffer; Nylén, Urban; Rutberg, Hans; Soop, Michael. Exploring similarities and differences in hospital adverse event rates between Norway and Sweden using Global Trigger Tool. BMJ Open (ISSN 2044-6055). 7(3) doi: 10.1136/bmjopen-2016-012492. 2017. Full-text

Løkse, Sigurd; Bianchi, Filippo Maria; Salberg, Arnt Børre; Jenssen, Robert. Spectral clustering using PCKID ? A probabilistic cluster kernel for incomplete data. Lecture Notes in Computer Science (ISSN 0302-9743). 10269 LNCS pp 431-442. doi: 10.1007/978-3-319-59126-1_36. 2017.

Tvete, Ingunn Fride; Bjørner, Trine; Skomedal, Tor. New benzodiazepine and Z-hypnotic users and disability pension: an eight-year nationwide observational follow-up study. Scandinavian Journal of Primary Health Care (ISSN 0281-3432). doi: 10.1080/02813432.2017.1358436. 2017. Full-text

Lison, Pierre; Kennington, Casey. Incremental Processing for Neural Conversational Models. SemDial Proceedings (ISSN 2308-2275). pp 162-163. 2017. Full-text

Lison, Pierre; Bibauw, Serge. Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models. In: 18th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2017). (ISBN 978-1-945626-82-1). pp 384-394. 2017. Full-text

Publications in 2017, 2016, 2015, 2014, 2013, earlier years