Sökning: "Probability and Inference Theory Group"
Visar resultat 1 - 5 av 12 avhandlingar innehållade orden Probability and Inference Theory Group.
1. Statistical inference and time-frequency estimation for non-stationary signal classification
Sammanfattning : This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. LÄS MER
2. Random Geometry and Reinforced Jump Processes
Sammanfattning : This thesis comprises three papers studying several mathematical models related to geometric Markov processes and random processes with reinforcements. The main goal of these works is to investigate the dynamics as well as the limiting behaviour of the models as time goes to infinity, the existence of invariant measures and limiting distributions, the speed of convergence and other interesting relevant properties. LÄS MER
3. Modelling and Inference using Locally Stationary Processes : Biomedical applications
Sammanfattning : This thesis considers statistical methods for non-stationary signals, specifically stochastic modelling, inference on the model parameters and optimal spectral estimation. The models are based on Silverman’s definition of Locally Stationary Processes (LSPs). LÄS MER
4. Non-parametric methods for functional data
Sammanfattning : In this thesis we develop and study non-parametric methods within three major areas of functional data analysis: testing, clustering and prediction. The thesis consists of an introduction to the field, a presentation and discussion of the three areas, and six papers. LÄS MER
5. Bayesian Cluster Analysis : Some Extensions to Non-standard Situations
Sammanfattning : 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. LÄS MER