Sökning: "Gaussian Markov random fields"

Visar resultat 1 - 5 av 10 avhandlingar innehållade orden Gaussian Markov random fields.

  1. 1. Scalable Bayesian spatial analysis with Gaussian Markov random fields

    Författare :Per Sidén; Mattias Villani; Anders Eklund; Håvard Rue; Linköpings universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Spatial statistics; Bayesian statistics; Gaussian Markov random fields; fMRI; Machine learning; Spatial statistik; Bayesiansk statistik; Gaussiska Markov-fält; fMRI; Maskininlärning;

    Sammanfattning : Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. LÄS MER

  2. 2. Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties

    Författare :David Bolin; Matematisk statistik; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; random fields; Gaussian Markov random fields; Matérn covariances; stochastic partial differential equations; Computational efficiency;

    Sammanfattning : The focus of this work is on the development of new random field models and methods suitable for the analysis of large environmental data sets. A large part is devoted to a number of extensions to the newly proposed Stochastic Partial Differential Equation (SPDE) approach for representing Gaussian fields using Gaussian Markov Random Fields (GMRFs). LÄS MER

  3. 3. Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling

    Författare :Johan Lindström; Matematisk statistik; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; vegetation; time series analysis; video segmentation; spatio-temporal modelling; precipitation; Markov chain Monte Carlo; Gaussian Markov random fields; expectation maximisation; change point detection; Bayesian recursive estimation; African Sahel; adaptive Gaussian mixtures;

    Sammanfattning : In this thesis computationally intensive methods are used to estimate models and to make inference for large, spatio-temporal data sets. The thesis is divided into two parts: the first two papers are concerned with video analysis, while the last three papers model and investigate environmental data from the Sahel area in northern Africa. LÄS MER

  4. 4. Satistical Modelling Of CO2 Exchange Between Land And Atmosphere : Using Stochastic Optimisation And Gaussian Markov Random Fields

    Författare :Unn Dahlén; Spatio-Temporal Stochastic Modelling Group; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Gaussiska Markovfält; Stokastisk optimering; CO2-flöden; Spatial statistik; Stochastic Optimisation; Gaussian Markov Random Fields; Atmospheric inverse modelling; Spatial statistics; Computational statistics;

    Sammanfattning : This thesis focuses on the development and application of efficient mathematicaltools for estimating and modelling the exchange of carbon dioxide (CO2) between the Earth and its atmosphere; here referred to as the global CO2 surface flux.There are two main approaches for estimating the CO2 flux: Processed based(bottom-up) modelling and atmospheric inversion (top-down) modelling. LÄS MER

  5. 5. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes

    Författare :Anders Hildeman; Göteborgs universitet; Göteborgs universitet; Gothenburg University; []
    Nyckelord :NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Substitute CT; Finite mixture models; Spatial statistics; Gaussian fields;

    Sammanfattning : Finite mixture models have proven to be a great tool for both modeling non-standard probability distributions and for classification problems (using the latent variable interpretation). In this thesis we are building spatial models by incorporating spatially dependent categorical latent random fields in a hierarchical manner similar to that of finite mixture models. LÄS MER