标题： 使用普罗克鲁斯特斯和构型模型的无标签点集的贝叶斯匹配 显示英文标题

标题： Bayesian matching of unlabelled point sets using Procrustes and configuration models

摘要： 使用贝叶斯推断匹配无标签点集的问题被考虑。 比较了两种最近提出的似然模型，基于Procrustes尺度形状和完整配置。 使用马尔可夫链蒙特卡洛模拟进行点集匹配的贝叶斯推断。 提出了一种改进的现有Procrustes算法，通过在预热期偶尔进行大的跳跃来提高收敛速度。 在模拟研究和实际数据中比较了Procrustes方法和配置方法，其中感兴趣的是估计蛋白质结合位点之间的匹配强度。 两种方法的性能通常非常相似，并使用拉普拉斯近似建立了两种模型之间的联系。 显示英文摘要

摘要： The problem of matching unlabelled 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.