Blar i NTNU Open på forfatter "Steinsland, Ingelin"
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A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records
Roksvåg, Thea; Steinsland, Ingelin; Engeland, Kolbjørn (Peer reviewed; Journal article, 2022)We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model. The simulations are treated as a covariate and the regression coefficient is modeled ... -
Hierarchical Modelling of Haplotype Effects on a Phylogeny
Selle, Maria Lie; Steinsland, Ingelin; Lindgren, Finn; Brajkovic, Vladimir; Cubric-Curik, Vlatka; Gorjanc, Gregor (Peer reviewed; Journal article, 2020)We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, ... -
Inflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles
Kleiven, Andreas; Steinsland, Ingelin (Chapter, 2019)This paper contributes to forecasting of renewable infeed for use in dispatch scheduling and power systems analysis. Ensemble predictions are commonly used to assess the uncertainty of a future weather event, but they often ... -
Is my study system good enough? A case study for identifying maternal effects
Holand, Anna Marie; Steinsland, Ingelin (Journal article; Peer reviewed, 2016)In this paper, we demonstrate how simulation studies can be used to answer questions about identifiability and consequences of omitting effects from a model. The methodology is presented through a case study where ... -
Journal of the Royal Statistical Society: Series C (Applied Statistics) Journal of the Royal Statistical Society: Series C (Applied Statistics) ORIGINAL ARTICLE Open Access A two-field geostatistical model combining point and areal observations—A case study of annual runoff predictions in the Voss area
Roksvåg, Thea; Steinsland, Ingelin; Engeland, Kolbjørn (Peer reviewed; Journal article, 2021)We estimate annual runoff by using a Bayesian geostatistical model for interpolation of hydrological data of different spatial support: streamflow observations from catchments (areal data), and precipitation and evaporation ... -
Markov chain Monte Carlo updating schemes for hidden Gaussian Markov random field models
Steinsland, Ingelin (Dr. ingeniøravhandling, 0809-103X; 2003:89, Doctoral thesis, 2003)Part I discusses how to construct approximations to the posterior distribution π(x|y, θ) of a latent Gaussian Markov random field on a graph of dimension n when data are considered conditionally mutually independent and ... -
Mixture models for flood frequency analysis - A case study for Norway
Hindenes, Silje (Master thesis, 2017)Flood frequency analysis (FFA) concerns prediction of the magnitude and corresponding frequency of extreme flood events. Extreme floods can be the result of various hydrological processes. In Norway, rainfall and snowmelt ... -
Modeling and inference for Bayesian animal models in the presence of non-ignorable missing data.: The shared random effects method.
Larsen, Camilla Thorrud (Master thesis, 2010)Missing data in quantitative genetic studies of wild populations poses a non-trivial issue. To obtain unbiased inferences in the presence of non-ignorable missing data, it is necessary to model the joint distribution of ... -
Modeling dependency structures in 450k DNA methylation data
Nustad, Haakon Egdetveit; Steinsland, Ingelin; Ollikainen, Miina; Cazaly, Emma; Kaprio, Jaakko; Benjamini, Yuval; Gervin, Kristina; Lyle, Robert (Peer reviewed; Journal article, 2022)Motivation DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences ... -
Modeling Passenger Count Data Based on Automatic Counting
Liabø, Simon (Master thesis, 2023)Moderne offentlige transportkjøretøy er utstyrt med en rekke sensorer og genererer store mengder data, som Automatisk Kjøretøy Lokalisering (Automatic Vehicle Location, AVL) og Automatisk Passasjertelling (Automatic Passenger ... -
Modelling and Inference for Bayesian Bivariate Animal Models using Integrated Nested Laplace Approximations
Bøhn, Eirik Dybvik (Master thesis, 2014)In this study we focus on performing inference on bivariate animal models using Integrated Nested Laplace Approximation (INLA). INLA is a methodology for making fast non-sampling based Bayesian inference for hierarchical ... -
Modelling diurnal temperature range in Norway: Statistical modelling and spatial interpolation with the Five-Parameter Lambda Distribution
Vandeskog, Silius Mortensønn (Master thesis, 2019)Vi modellerer fordelingen av den døgnlige variasjonsbredden til temperatur med femparameter-lambdafordelingen (FPLF). Både lokal og regional modelltilpasning av en FPLF utføres. Lokal modelltilpasning blir utført ved hjelp ... -
Multivariate Gaussian Random Fields: The Stochastic Partial Differential Equation approach
Hu, Xiangping (Doctoral Theses at NTNU, 1503-8181; 2013:206, Doctoral thesis, 2013) -
Novel statistical variance and dependency models in quantitative genetics: Enabled by recent inference methods
Selle, Maria Lie (Doctoral theses at NTNU;2020:217, Doctoral thesis, 2020) -
On estimation and identifiability issues of sex-linked inheritance with a case study of pigmentation in Swiss barn owl (Tyto alba)
Larsen, Camilla Thorrud; Holand, Anna Marie; Jensen, Henrik; Steinsland, Ingelin; Roulin, Alexandre (Journal article; Peer reviewed, 2014)Genetic evaluation using animal models or pedigree-based models generally assume only autosomal inheritance. Bayesian animal models provide a flexible framework for genetic evaluation, and we show how the model readily can ... -
Physics-Informed Bayesian Calibration Accounting for Model Discrepancy in a Linearized Homogeneous Ordinary Differential Equation
Flo, Selma Lerkerød (Master thesis, 2023)Denne masteroppgåva utforskar eit fysikkinformert, fullstendig bayesiansk rammeverk for parameterestimering og usikkerheitsanalyse i lineariserte homogene ordinære differensiallikningar (ODE-ar). Vi undersøkjer korleis eit ... -
Physics-Informed Statistical Machine Learning and Methods for Digital Twins
Spitieris, Michail (Doctoral theses at NTNU;2023:177, Doctoral thesis, 2023) -
Pre- and postprocessing flood forecasts using Bayesian model averaging
Hegdahl, Trine Jahr; Engeland, Kolbjørn; Steinsland, Ingelin; Singleton, Andrew (Peer reviewed; Journal article, 2023)In this study, pre- and postprocessing of hydrological ensemble forecasts are evaluated with a special focus on floods for 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature and precipitation ... -
Predicting Snow Density
Færevåg, Åshild (Master thesis, 2013)Snow density is an important measure in hydrological applications. It is used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm et al. (2010) predicts the snow density by using snow depth, ... -
Prediction models for hypertension using the HUNT Study data
Schjerven, Filip (Master thesis, 2020)I denne oppgaven sammenlignes forskjellige modellfamiliers evne til å predikere 11-års risikoen for binær hypertensjon status, ved bruk av data fra helseundersøkelsen i Trøndelag, HUNT. Modellfamiliene som ble valgt var ...