Blar i NTNU Open på forfatter "Rue, Håvard"

Viser treff 1-20 av 35

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

      Kolberg, Sjur; Rue, Håvard; Gottschalk, Lars (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 

      Stenerud, Snorre Gebhardt (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) 

      Illian, Janine; Sørbye, Sigrunn Holbek; Rue, Håvard (Journal article; Peer reviewed, 2012)

    • Approximate Bayesian inference for spatial econometrics models 

      Bivand, Roger; Gómez-Rubio, Virgilio; Rue, Håvard (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 

      Rue, Håvard; Riebler, Andrea Ingeborg; Sørbye, Sigrunn Holbek; Illian, Janine B.; Simpson, Daniel Peter; Lindgren, Finn Kristian (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 

      Guo, Jingyi (Doctoral theses at NTNU;2016:342, Doctoral thesis, 2016)

    • Bayesian multiscale analysis of images modeled as Gaussian Markov random fields 

      Thon, Kevin; Rue, Håvard; Skrøvseth, Stein Olav; Godtliebsen, Fred (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 

      Nordahl, Thor Mikkel (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 

      Ramsnes, Kristine Behné (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 

      Myrvoll-Nilsen, Eirik (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 

      Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn; Rue, Håvard (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 

      Bjormyr, Tobias Liland (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 

      Andersen, Espen Teie (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 ...

    • Estimating tukey depth using incremental quantile estimators 

      Hammer, Hugo Lewi; Yazidi, Anis; Rue, Håvard (Peer reviewed; Journal article, 2022)
      Measures of distance or how data points are positioned relative to each other are fundamental in pattern recognition. The concept of depth measures how deep an arbitrary point is positioned in a dataset, and is an interesting ...

    • Gaussian Markov Models for Adaptive Smoothing 

      Ingebrigtsen, Rikke (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 

      Kvamme, Håvard (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 ...

    • Importance Sampling with the Integrated Nested Laplace Approximation 

      Berild, Martin Outzen; Martino, Sara; Gómez-Rubio, Virgilio; Rue, Håvard (Peer reviewed; Journal article, 2022)
      The integrated nested Laplace approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation of posterior marginals for the parameters ...

    • Intuitive Joint Priors for Variance Parameters 

      Fuglstad, Geir-Arne; Hem, Ingeborg Gullikstad; Knight, Alexander; Rue, Håvard; Riebler, Andrea Ingeborg (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 

      Rogvin, Maria Beite (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 

      Bakka, Haakon (Doctoral theses at NTNU;2017:69, Doctoral thesis, 2017)