Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo

@article{Tucker2021MultimodalBR,
  title={Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo},
  author={J. D. Tucker and Lyndsay Shand and Kenny Chowdhary},
  journal={Comput. Stat. Data Anal.},
  year={2021},
  volume={163},
  pages={107298}
}
Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data… Expand
Registration for Incomplete Non-Gaussian Functional Data
Accounting for phase variability is a critical challenge in functional data analysis. To separate it from amplitude variation, functional data are registered, i.e., their observed domains areExpand

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