Bayesian computational methods for spatial analysis of images

Moores, Matthew T. (2015) Bayesian computational methods for spatial analysis of images. PhD by Publication, Queensland University of Technology.


This thesis introduces a new way of using prior information in a spatial model and develops scalable algorithms for fitting this model to large imaging datasets. These methods are employed for image-guided radiation therapy and satellite based classification of land use and water quality. This study has utilized a pre-computation step to achieve a hundredfold improvement in the elapsed runtime for model fitting. This makes it much more feasible to apply these models to real-world problems, and enables full Bayesian inference for images with a million or more pixels.

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8 since deposited on 12 Oct 2015
8 in the past twelve months

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ID Code: 84728
Item Type: QUT Thesis (PhD by Publication)
Supervisor: Mengersen, Kerrie & Harden, Fiona
Keywords: Bayesian statistics, image analysis, computational statistics, computed tomography, Markov random field, Potts/Ising model, longitudinal imaging, Approximate Bayesian Computation, Sequential Monte Carlo, Intractable Likelihood
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 12 Oct 2015 03:12
Last Modified: 05 Jul 2016 03:38

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