Mechanistic Hierarchical Gaussian Processes.

Journal Article (Journal Article)

The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle physiology. A novel class of hierarchical Gaussian processes is defined that favors curves consistent with differential equations defined on motor, damper, spring systems. A Gibbs sampler is proposed to sample from the posterior distribution and applied to a study of rats exposed to non-injurious muscle activation protocols. Although motivated by muscle force data, a parallel approach can be used to include mechanistic information in broad functional data analysis applications.

Full Text

Duke Authors

Cited Authors

  • Wheeler, MW; Dunson, DB; Pandalai, SP; Baker, BA; Herring, AH

Published Date

  • July 2014

Published In

Volume / Issue

  • 109 / 507

Start / End Page

  • 894 - 904

PubMed ID

  • 25541568

Pubmed Central ID

  • PMC4273873

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2014.899234


  • eng