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.
Last 5 scientific articles
Heinrich, Claudio Constantin. On the number of bins in a rank histogram. Quarterly Journal of the Royal Meteorological Society (ISSN 0035-9009). doi: 10.1002/qj.3932. 2020.
Gilbert, Andrew David; Holden, Marit; Eikvil, Line; Rakhmail, Mariia; Babic, Aleksandar; Aase, Svein Arne; Samset, Eigil; Mcleod, Kristin. User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing. IEEE journal of biomedical and health informatics (ISSN 2168-2194). doi: 10.1109/JBHI.2020.3029392. 2020. Institutional archive
Trier, Øivind Due; Reksten, Jarle Hamar; Løseth, Kristian. Automated mapping of cultural heritage in Norway from airborne lidar data using faster R-CNN. International Journal of Applied Earth Observation and Geoinformation (ISSN 1569-8432). 95 doi: 10.1016/j.jag.2020.102241. 2020.
Engebretsen, Solveig; Glad, Ingrid Kristine. Partially linear monotone methods with automatic variable selection and monotonicity direction discovery. Statistics in Medicine (ISSN 0277-6715). 39(25) pp 3549-3568. doi: 10.1002/sim.8680. 2020.
Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert. Learning latent representations of bank customers with the Variational Autoencoder. Expert systems with applications (ISSN 0957-4174). doi: 10.1016/j.eswa.2020.114020. 2020.