Bayesian matching of unlabeled point sets using procrustes and configuration models

Kim Kenobi*, Ian L. Dryden

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (SciVal)


The problem of matching unlabeled point sets using Bayesian inference is considered. Two recently proposed models for the likelihood are compared, based on the Procrustes size-and-shape and the full configuration. Bayesian inference is carried out for matching point sets using Markov chain Monte Carlo simulation. An improvement to the existing Procrustes algorithm is proposed which improves convergence rates, using occasional large jumps in the burn-in period. The Pro-crustes and configuration methods are compared in a simulation study and using real data, where it is of interest to estimate the strengths of matches between protein binding sites. The performance of both methods is generally quite similar, and a connection between the two models is made using a Laplace approximation.

Original languageEnglish
Pages (from-to)547-566
Number of pages20
JournalBayesian Analysis
Issue number3
Publication statusPublished - 2012
Externally publishedYes


  • Gibbs
  • Markov chain Monte Carlo
  • Metropolis-hastings
  • Molecule
  • Procrustes
  • Protein
  • Shape
  • Size


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