Sökning: "Gibbs sampler"
Visar resultat 6 - 10 av 11 avhandlingar innehållade orden Gibbs sampler.
6. Bayesian inference in probabilistic graphical models
Sammanfattning : This thesis consists of four papers studying structure learning and Bayesian inference in probabilistic graphical models for both undirected and directed acyclic graphs (DAGs).Paper A presents a novel algorithm, called the Christmas tree algorithm (CTA), that incrementally construct junction trees for decomposable graphs by adding one node at a time to the underlying graph. LÄS MER
7. Novel methods for improved tree breeding
Sammanfattning : The development and implementation of statistical tools to improve inference in sustainable forest tree breeding are presented here. By combining classical quantitative genetic theory and novel statistical methods, a number of parameters are optimized. LÄS MER
8. System identification with input uncertainties : an EM kernel-based approach
Sammanfattning : Many classical problems in system identification, such as the classical predictionerror method and regularized system identification, identification of Hammersteinand cascaded systems, blind system identification, as well as errors-in-variablesproblems and estimation with missing data, can be seen as particular instancesof the general problem of the identification of systems with limited information.In this thesis, we introduce a framework for the identification of linear dynamicalsystems subject to inputs that are not perfectly known. LÄS MER
9. Stochastic Models Involving Second Order Lévy Motions
Sammanfattning : This thesis is based on five papers (A-E) treating estimation methods for unbounded densities, random fields generated by Lévy processes, behavior of Lévy processes at level crossings, and a Markov random field mixtures of multivariate Gaussian fields. In Paper A we propose an estimator of the location parameter for a density that is unbounded at the mode. LÄS MER
10. Some Contributions to Heteroscedastic Time Series Analysis and Computational Aspects of Bayesian VARs
Sammanfattning : Time-dependent volatility clustering (or heteroscedasticity) in macroeconomic and financial time series has been analyzed for more than half a century. The inefficiencies it causes in various inference procedures are well known and understood. Despite this, heteroscedasticity is surprisingly often neglected in practical work. LÄS MER