Bayesian Registration of Functions With a Gaussian Process Prior
Bayesian Registration of Functions With a Gaussian Process Prior
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DOI：
10.1080/10618600.2017.1336444
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发表时间：
2017-01-01
影响因子：
2.4
通讯作者：
Kurtek, Sebastian
中科院分区：
文献类型：
--
作者：
Lu, Yi;Herbei, Radu;Kurtek, Sebastian
我们提出了一个贝叶斯框架注册的实值功能数据。在我们的方法的核心是一系列的数据和功能参数的转换，微分几何框架下开发的。我们的目标是尽可能长时间地避免函数对象的离散化，从而最大限度地减少与高维贝叶斯推理相关的潜在陷阱。使用一种新的马尔可夫链蒙特卡罗（MCMC）算法，这是非常适合于估计的功能，从后验分布的近似提请。我们说明了我们的方法，通过成对和多功能数据注册，使用模拟和真实的数据集。这篇文章的补充材料可在网上查阅。
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.