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Dynamic Bayesian models for modelling environmental space-time fields

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Title: Dynamic Bayesian models for modelling environmental space-time fields
Author: Dou, Yiping
Degree Doctor of Philosophy - PhD
Program Statistics
Copyright Date: 2008
Publicly Available in cIRcle 2008-04-01
Subject Keywords Bayesian hierarchical modelling; Kalman filter; Dynamic linear modelling; Bayesian spatial prediction; MCMC algorithm; Gibbs sampling; Wishart distributions; Forward filtering; Backward sampling; Bayesian spatial prediction; Bayesian empirical orthogonal functions; Bayesian spatial prediction methods
Abstract: This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine- scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and multivariate responses. The result generalizes a number of current approaches in this field.
URI: http://hdl.handle.net/2429/634

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