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Title: Bayesian inference and deconvolution
Author: Adami, K. Z.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2004
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This thesis is concerned with the development of Bayesian methods for inference and deconvolution. We compare and contrast different Bayesian methods for model selection, specifically Markov Chain Monte Carlo methods (MCMC) and Variational methods and their application to medical and industrial problems. In chapter 1, the Bayesian framework is outlined. In chapter 2 the different methods for Bayesian model selection are introduced and we assess each method in turn. Problems with MCMC methods and Variational methods are highlighted, before a new method which combines the strengths of both the MCMC methods and the Variational methods is developed. Chapter 3 applies the inferential methods described in chapter 2 to the problem of interpolation, before a regression neural network is implemented and tested on a set of data from the microelectronics industry. Chapter 4 applies the interpolation methods developed in chapter three to characterise the electrical nature of the testing site in the integrated circuit (IC) manufacturing process. Chapter 5 describes Independent Component Analysis (ICA) as a solution to the bilinear decomposition problem and its application to Magnetic Resonance Imaging. This chapter also compares and contrasts various Bayesian algorithms for the bilinear problem with a non-Bayesian MUSIC algorithm. Chapter 6 describes various models for the deconvolution of images including a regression network. The ICA model of chapter 5 is then extended to the deconvolution and blind deconvolution problems with the addition of intrinsic correlation functions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available