Bayesian Analysis

Bayesian Matching of Unlabeled Point Sets Using Procrustes and Configuration Models

Kim Kenobi and Ian L. Dryden

Full-text: Open access

Abstract

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 Procrustes 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.

Article information

Source
Bayesian Anal. Volume 7, Number 3 (2012), 547-566.

Dates
First available in Project Euclid: 28 August 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1346158775

Digital Object Identifier
doi:10.1214/12-BA718

Mathematical Reviews number (MathSciNet)
MR2981627

Zentralblatt MATH identifier
1330.62138

Keywords
Gibbs Markov chain Monte Carlo Metropolis-Hastings molecule protein Procrustes size shape

Citation

Kenobi, Kim; Dryden, Ian L. Bayesian Matching of Unlabeled Point Sets Using Procrustes and Configuration Models. Bayesian Anal. 7 (2012), no. 3, 547--566. doi:10.1214/12-BA718. https://projecteuclid.org/euclid.ba/1346158775


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