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Activity Number: 455 - Recent Advances in Bayesian Computation: Theory and Methods
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #307030
Title: Bayesian Registration of Functions with a Gaussian Process Prior
Author(s): Radu Herbei* and Yi Lu and Sebastian Kurtek
Companies: Ohio State University and Drew University and The Ohio State University
Keywords: functional data; MCMC

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.

Authors who are presenting talks have a * after their name.

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