Search for dissertations about: "Gaussian mixture model"
Showing result 1 - 5 of 40 swedish dissertations containing the words Gaussian mixture model.
-
1. Statistical methods in medical image estimation and sparse signal recovery
Abstract : This thesis presents work on methods for the estimation of computed tomography (CT) images from magnetic resonance (MR) images for a number of diagnostic and therapeutic workflows. The study also demonstrates sparse signal recovery method, which is an intermediate method for magnetic resonance image reconstruction. READ MORE
-
2. Model Based Speech Enhancement and Coding
Abstract : In mobile speech communication, adverse conditions, such as noisy acoustic environments and unreliable network connections, may severely degrade the intelligibility and natural- ness of the received speech quality, and increase the listening effort. This thesis focuses on countermeasures based on statistical signal processing techniques. READ MORE
-
3. Bayesian Cluster Analysis : Some Extensions to Non-standard Situations
Abstract : The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. READ MORE
-
4. 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
-
5. On flexible random field models for spatial statistics: Spatial mixture models and deformed SPDE models
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