Bayesian Registration of Functions With a Gaussian Process Prior
提出一个贝叶斯框架来配准实值函数数据，通过微分几何变换避免过早离散化，并用新MCMC算法从后验分布中抽样，适用于成对和多个函数数据的配准。
We present a Bayesian framework for registration of real-valued functional data. At the core of our approach is a series of transformations of the data and functional parameters, developed under a differential geometric framework. We aim to avoid discretization of functional objects for as long as possible, thus minimizing the potential pitfalls associated with high-dimensional Bayesian inference. Approximate draws from the posterior distribution are obtained using a novel Markov chain Monte Carlo (MCMC) algorithm, which is well suited for estimation of functions. We illustrate our approach via pairwise and multiple functional data registration, using both simulated and real datasets. Supplementary material for this article is available online.