Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo

@inproceedings{Tucker2020MultimodalBR,
  title={Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo},
  author={J. D. Tucker and Lyndsay Shand and Kenny Chowdhary},
  year={2020}
}
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

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