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Durham e-Theses
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A Bayes Linear Approach to Making Inferences from X-rays

LOPEZ, BENJAMIN,THOMAS (2018) A Bayes Linear Approach to Making Inferences from X-rays. Doctoral thesis, Durham University.

Full text not available from this repository.
Author-imposed embargo until 15 January 2021.

Abstract

X-ray images are often used to make inferences about physical phenomena and the entities about which inferences are made are complex. The Bayes linear approach is a generalisation of subjective Bayesian analysis suited to uncertainty quantification for complex systems. Therefore, Bayes linear is an appropriate tool for making inferences from X-ray images.

In this thesis, I will propose methodology for making inferences about quantities, which may be organised as multivariate random fields. A number of problems will be addressed: anomaly detection, emulation, inverse problem solving and transferable databases. Anomaly detection is deciding whether a new observation belongs to the same population as a reference population, emulation is the task of building a statistical model of a complex computer model, inverse problem solving is the task of making inferences about system values, given an observation of system behaviour and transferable databases is the task of using a data-set created using a simulator to make inferences about physical phenomena.

The methods we use to address these problems will be exemplified using applications from the X-ray industry. Anomaly detection will be used to identify plastic contaminants in chocolate bars, emulation will be used to efficiently predict the scatter present in an X-ray image, inverse problem solving will be used to infer an entity's composition from an X-ray image and transferable databases will be used to improve image quality and return diagnostic measures from clinical X-ray images. The Bayes linear approach to making inferences from an X-ray image enables improvements over the state-of-the-art approaches to high impact problems.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:Bayes linear methods, emulation, inverse problem solving, history matching, iterative history matching, non-stationary-non-isotropic scatter prediction, anomaly detection, scatter correction, reification, X-ray imaging, GEANT4.
Faculty and Department:Faculty of Science > Mathematical Sciences, Department of
Thesis Date:2018
Copyright:Copyright of this thesis is held by the author
Deposited On:18 Jan 2019 10:15

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