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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|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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