Modeling climate variables using Bayesian finite mixture models
| dc.contributor.advisor | Keitt, Timothy H. | en |
| dc.contributor.committeeMember | Müller, Peter | en |
| dc.creator | Cuthbertson, Thomas Edwin | en |
| dc.date.accessioned | 2015-11-16T19:00:51Z | en |
| dc.date.available | 2015-11-16T19:00:51Z | en |
| dc.date.issued | 2015-05 | en |
| dc.date.submitted | May 2015 | en |
| dc.date.updated | 2015-11-16T19:00:51Z | en |
| dc.description | text | en |
| dc.description.abstract | This paper presents an alternative to point-based clustering models using a Bayesian finite mixture model. Using a simulation of soil moisture data in the Amazon region of South America, a Bayesian mixture of regressions is used to preserve periodic behavior within clusters. The mixture model provides a full probabilistic description of all uncertainties in the parameters that generated the data in addition to a clustering algorithm which better preserves the periodic nature of data at a particular pixel. | en |
| dc.description.department | Statistics | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | doi:10.15781/T2HD0V | en |
| dc.identifier.uri | http://hdl.handle.net/2152/32499 | en |
| dc.language.iso | en | en |
| dc.subject | Bayesian finite mixture model | en |
| dc.subject | Climate simulation | en |
| dc.subject | Hierarchical models | en |
| dc.subject | Grid approximation | en |
| dc.subject | Bayes | en |
| dc.subject | Bayesian | en |
| dc.subject | Gibbs sampling | en |
| dc.title | Modeling climate variables using Bayesian finite mixture models | en |
| dc.type | Thesis | en |
| thesis.degree.department | Statistics | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Masters | en |
| thesis.degree.name | Master of Science in Statistics | en |