Sökning: "Bayesian inference"
Visar resultat 1 - 5 av 97 avhandlingar innehållade orden Bayesian inference.
- Detta är en avhandling från Stockholm : Department of Mathematics, Stockholm University
Sammanfattning : Phylogenetics is the study of the evolutionary relationship between species. Inference of phylogeny relies heavily on statistical models that have been extended and refined tremendously over the past years into very complex hierarchical models. LÄS MER
- Detta är en avhandling från Göteborg : Chalmers University of Technology
Sammanfattning : Exact inference on Bayesian networks has been developed through sophisticated algorithms. One of which, the variable elimination algorithm, identifies smaller components of the network, called factors, on which local operations are performed. In principle this algorithm can be used on any Bayesian network. LÄS MER
- Detta är en avhandling från Stockholm University
Sammanfattning : In this thesis we consider two very different topics in Bayesian phylogenetic inference. The first paper, "Inferring speciation and extinction rates under different sampling schemes" by Sebastian Höhna, Tanja Stadler, Fredrik Ronquist and Tom Britton, focuses on estimating the rates of speciation and extinction of species when only a subsample of the present day species is available. LÄS MER
- Detta är en avhandling från Stockholm : Department of Statistics, Stockholm University
Sammanfattning : In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges traditional modeling and inference methodology. LÄS MER
- Detta är en avhandling från Stockholm : Statistiska institutionen
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