MAD-Bayes matching and alignment for labelled and unlabelled configurations

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Geometry Driven Statistics, 2015, pp. 377 - 390
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© 2015 John Wiley & Sons, Ltd. All rights reserved. Professor Kanti Mardia has made numerous original contributions to an area of unlabelled shape analysis inspired by matching and alignment problems arising in protein bioinformatics. In this chapter, a Bayesian model proposed by Mardia and Green in 2006, and others related to it, are revisited to investigate the potential for using modern optimisation algorithms to expedite calculations of properties of the posterior distribution, in place of the Monte Carlo computational methods originally proposed.
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