Search for dissertations about: "Anders Hildeman"

Found 3 swedish dissertations containing the words Anders Hildeman.

  1. 1. On flexible random field models for spatial statistics: Spatial mixture models and deformed SPDE models

    Author : Anders Hildeman; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Spatial statistics; Significant wave height; Spatial mixture model; Stochastic partial differential equation; Log-Gaussian Cox process; Point process; Gaussian random field; Substitute-CT;

    Abstract : Spatial random fields are one of the key concepts in statistical analysis of spatial data. The random field explains the spatial dependency and serves the purpose of regularizing interpolation of measured values or to act as an explanatory model. READ MORE

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

    University dissertation from Gothenburg : Chalmers tekniska högskola

    Author : Anders Hildeman; [2017]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Spatial statistics; Substitute CT; Finite mixture models; Gaussian fields;

    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

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

    University dissertation from Gothenburg : Chalmers University of Technolog

    Author : Anders Hildeman; Göteborgs universitet.; Gothenburg University.; [2017]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Substitute CT; Finite mixture models; Spatial statistics; Gaussian fields;

    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