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This is a In Vitro Study study (also classified as: Observational).

Deep Neural Network for Functional Graphical Models Structure Learning

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Proposed approach estimates neighborhoods in multivariate functional data, indicating improved model flexibility.

The approach captures conditional independence structures in multivariate functional data effectively.
This method avoids strict distributional assumptions and adapts well to various functional data conditions.
Utilizing deep neural networks facilitates flexible regression methodologies for data representation.
Model consistency is established, achieving convergence rates comparable to standard nonparametric regression.

Wang et al. (2025) studied this question.

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