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dc.contributor.advisorTjelmeland, Håkon
dc.contributor.authorLoe, Margrethe Kvale
dc.date.accessioned2021-09-15T07:27:32Z
dc.date.available2021-09-15T07:27:32Z
dc.date.issued2021
dc.identifier.isbn978-82-326-5252-5
dc.identifier.issn2703-8084
dc.identifier.urihttps://hdl.handle.net/11250/2777303
dc.language.isoengen_US
dc.publisherNTNUen_US
dc.relation.ispartofseriesDoctoral theses at NTNU;2021:303
dc.relation.haspartPaper 1: Loe, Margrethe Kvale; Tjelmeland, Håkon. Ensemble updating of binary state vectors by maximising the expected number of unchanged components. Scandinavian Journal of Statistics. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.relation.haspartPaper 2: Loe, Margrethe Kvale; Grana, Dario; Tjelmeland, Håkon. Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors. Mathematical Geosciences 2021 ;Volum 53. s. 325-347 https://doi.org/10.1007/s11004-021-09922-4 This article is licensed under a Creative Commons Attribution 4.0 International Licenseen_US
dc.relation.haspartPaper 3: Loe, Margrethe Kvale; Tjelmeland, Håkon. A generalised and fully Bayesian ensemble updating frameworken_US
dc.relation.haspartPaper 4: Loe, Margrethe Kvale; Tjelmeland, Håkon. Ensemble updating of categorical state vectorsen_US
dc.titleEnsemble updating for a state-space model with categorical variablesen_US
dc.typeDoctoral thesisen_US
dc.subject.nsiVDP::Mathematics and natural science: 400::Mathematics: 410en_US


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