Adeegso tilmaantan si aad u carrabbaabdo ama ugu samayso link qoraalkan
http://hdl.handle.net/2307/5913
Cinwaan: | Temporal and spatio-temporal modes for circular and circular-linear data | Qore: | Mastrantonio, Gianluca | Ereyga furaha: | Cicular data Spatio-temporal process Process Hidden Markov model |
Taariikhda qoraalka: | 8-Apr-2016 | Tifaftire: | Università degli studi Roma Tre | Abstract: | Circular data arise naturally in many scientific fields, for example oceanography (wave directions), meteorology (wind directions), biology (animal movement). Due to the circular domain, to the sensitivity of descriptive and inferential results to the starting point and orientation on the circle, analysis of circular data is challenging. We propose models for temporal and spatio-temporal circular and circular-linear data. We show that under a Bayesian framework, the complex nature of circular data and the difficulties in a joint modelling of circular-linear variables can be easily overcome. Two main research frameworks are touched. The first deals with the build of spatio-temporal models for circular variables, while the second address topics in the joint temporal classification of circular-linear variables. In all the models proposed, exploiting data augmentation techniques, we are able to propose efficient, and easy to implement, Markov chain Monte Carlo algorithm. | URI : | http://hdl.handle.net/2307/5913 | Xuquuqda Gelitaanka: | info:eu-repo/semantics/openAccess |
Wuxuu ka dhex muuqdaa ururinnada: | Dipartimento di Economia T - Tesi di dottorato |
Muuji xogta qoraalka Ku tali qoraalkan
Page view(s)
128
Last Week
0
0
Last month
0
0
checked on Sep 19, 2024
Download(s)
110
checked on Sep 19, 2024
Google ScholarTM
Check
Dhammaan qoraallada lagu kaydiyay DSpace waxay u dhowrsanyihiin xuquuqda qoraha.