Search for dissertations about: "Gaussian Markov Random Fields"
Showing result 1 - 5 of 9 swedish dissertations containing the words Gaussian Markov Random Fields.
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1. Scalable Bayesian spatial analysis with Gaussian Markov random fields
Abstract : Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. READ MORE
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2. Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties
Abstract : 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). READ MORE
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3. Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling
Abstract : 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. READ MORE
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4. Satistical Modelling Of CO2 Exchange Between Land And Atmosphere : Using Stochastic Optimisation And Gaussian Markov Random Fields
Abstract : 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. READ MORE
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5. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes
Abstract : 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. READ MORE