# Bayesian Functional Principal Component Analysis using Relaxed Mutually Orthogonal Processes

@inproceedings{Matuk2022BayesianFP, title={Bayesian Functional Principal Component Analysis using Relaxed Mutually Orthogonal Processes}, author={James Matuk and Amy H. Herring and David B. Dunson}, year={2022} }

Functional Principal Component Analysis (FPCA) is a prominent tool to characterize variability and reduce dimension of longitudinal and functional datasets. Bayesian implementations of FPCA are advantageous because of their ability to propagate uncertainty in subsequent modeling. To ease computation, many modeling approaches rely on the restrictive assumption that functional principal components can be represented through a pre-speciﬁed basis. Under this assumption, inference is sensitive to…

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