Now showing items 1-20 of 32

• #### A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model ﻿

(Journal article; Peer reviewed, 2006)
A method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid distributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid ...
• #### A Study on Soccer Prediction using Goals and Shots on Target ﻿

(Master thesis, 2015)
In this thesis I have developed a model for result prediction in soccer. The model is based on chances created being modeled as a Poisson process while goals scored is seen as a result of first creating chances and then ...
• #### A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) ﻿

(Journal article; Peer reviewed, 2012)
• #### Approximate Bayesian inference for spatial econometrics models ﻿

(Journal article; Peer reviewed, 2014)
In this paper we explore the use of the Integrated Laplace Approximation (INLA) for Bayesian inference in some widely used models in Spatial Econometrics. Bayesian inference often relies on computationally intensive ...
• #### Bayesian Computing with INLA: A Review ﻿

(Journal article; Peer reviewed, 2017)
The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774). This simple idea ...
• #### Bayesian Meta-analysis ﻿

(Doctoral theses at NTNU;2016:342, Doctoral thesis, 2016)
• #### Bayesian multiscale analysis of images modeled as Gaussian Markov random fields ﻿

(Journal article; Peer reviewed, 2012)
A Bayesian multiscale technique for detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables ...
• #### Beregninger av Optimeringsproblemer i Statistisk Læring med TensorFlow ﻿

(Master thesis, 2016)
Modeller i statistisk læring, spesielt Nevrale Nettverk, har blitt anvendt med stor suksess på en rekke problemer i Kunstig Intelligens. Denne oppgaven beskriver og demonstrerer TensorFlow, som er en softwarepakke laget ...
• #### Changepoint Models ﻿

(Master thesis, 2009)
In this thesis we study recursion methods for making Bayesian inference on a class of multiple changepoint models, introduced in Fearnhead(2006). We present and implement recursion algorithms, and we evaluate how parameter ...
• #### Computationally efficient Bayesian approximation of fractional Gaussian noise using AR1 processes ﻿

(Master thesis, 2016)
The goal of this thesis is to explore a way of performing efficient Bayesian inference of fractional Gaussian noise series using the R-INLA framework. Finding the MLE of the Hurst exponent and the innovation variance of ...
• #### Constructing Priors that Penalize the Complexity of Gaussian Random Fields ﻿

(Journal article; Peer reviewed, 2018)
Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance ...
• #### Deep Learning with emphasis on extracting information from text data ﻿

(Master thesis, 2016)
In this thesis the Natural Language Processing (NLP) problems of predicting the negative or positive sentiment of a movie review (sentiment analysis) and Automated Essay Grading (AES) were analyzed. The data set used for ...
• #### Dynamic High Frequency Trading Models ﻿

(Master thesis, 2009)
This thesis considers constructing high-frequency quantitative trading models. The work is a continuation of my project thesis (spring 2009) and Birgitte Ringstad Vartdal's master thesis (2000). We build our trading model ...
• #### Gaussian Markov Models for Adaptive Smoothing ﻿

(Master thesis, 2010)
In this thesis, we study Gaussian Markov random field representation of the non-homogenous integrated Wiener process, for the purpose of doing adaptive smoothing of temporal data. We demonstrate that these representations ...
• #### Gender prediction on Norwegian Twitter accounts ﻿

(Master thesis, 2015)
In this thesis, methods for predicting the gender of Norwegian Twitter accounts were investigated. Through Twitterâ s public APIs, various account information is available. Tweets (text), personal descriptions, friends ...
• #### Intuitive Joint Priors for Variance Parameters ﻿

(Journal article; Peer reviewed, 2019)
Variance parameters in additive models are typically assigned independent priors that do not account for model structure. We present a new framework for prior selection based on a hierarchical decomposition of the total ...
• #### Markov Representation of Matérn Fields in one Dimension ﻿

(Master thesis, 2009)
In this thesis we study Markov representations of Matern Gaussian fields in one dimension. In particular, we discuss how boundary conditions could be imposed to control the marginal properties of the Markov field.
• #### Modeling Spatial Dependencies using Barriers and Different Terrains ﻿

(Doctoral theses at NTNU;2017:69, Doctoral thesis, 2017)
• #### Modelling Correlated Financial Time Series ﻿

(Master thesis, 2011)
Western Bulk is a shipping company that is interested in generating realistic realisations of five different price series; two freight rates, one interest rate and two oil rates. The realisations should keep the internal ...
• #### Multivariate DCC-GARCH Model: -With Various Error Distributions ﻿

(Master thesis, 2009)
In this thesis we have studied the DCC-GARCH model with Gaussian, Student's $t$ and skew Student's t-distributed errors. For a basic understanding of the GARCH model, the univariate GARCH and multivariate GARCH models in ...