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BAYESIAN COMPUTATIONAL METHODS FOR SPATIAL ANALYSIS OF IMAGES

Published online by Cambridge University Press:  08 January 2016

MATTHEW T. MOORES*
Affiliation:
University of Warwick, Coventry CV4 7AL, UK email [email protected]
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Abstract

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Type
Abstracts of Australasian PhD Theses
Copyright
© 2016 Australian Mathematical Publishing Association Inc. 

References

Moores, M. T., Drovandi, C. C., Mengersen, K. and Robert, C. P., ‘Pre-processing for approximate Bayesian computation in image analysis’, Stat. Comput. 25(1) (2015), 2333.CrossRefGoogle Scholar
Moores, M. T., Hargrave, C. E., Deegan, T., Poulsen, M., Harden, F. and Mengersen, K., ‘An external field prior for the hidden Potts model with application to cone-beam computed tomography’, Comput. Statist. Data Anal. 86 (2015), 2741.Google Scholar
Moores, M. T., Hargrave, C. E., Harden, F. and Mengersen, K., ‘Segmentation of cone-beam CT using a hidden Markov random field with informative priors’, Proc. 17th Int. Conf. ICCR, J. Phys.: Conf. Ser., Vol. 489 (Melbourne, Australia, 2014), 012076.Google Scholar
Moores, M. T. and Mengersen, K., ‘bayesImageS: Bayesian methods for image segmentation using a hidden Potts model’, R package version 0.1-21, 2015; http://researchdatafinder.qut.edu.au/display/n11957.Google Scholar
Moores, M. T., Pettitt, A. N. and Mengersen, K., ‘Scalable Bayesian inference for the inverse temperature of a hidden Potts model’, Preprint, 2015, arXiv:1503.08066; http://arxiv.org/abs/1503.08066.Google Scholar
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