Bayesian Approaches to Issues Arising in Spatial Modelling
Author(s) -
Earl Duncan
Publication year - 2017
Language(s) - English
Resource type - Dissertations/theses
DOI - 10.5204/thesis.eprints.112356
Subject(s) - smoothing , bayesian probability , data science , computer science , spatial analysis , statistical model , data mining , management science , econometrics , machine learning , artificial intelligence , statistics , mathematics , engineering , computer vision
This thesis addressed several contemporary issues arising in the analysis of spatial data and the broader statistical methodology. Two state-of-the-art statistical models are developed for the purpose of identifying unusual trends, a new algorithm to deal with label switching is devised which outperforms existing solutions, and new approaches to spatial smoothing are explored. The outcomes from this thesis should be of interest to managers in the health sector, biostatisticians, and researchers who deal with spatial data
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