Closed-Form Solution of Fully-Observed Variational Inference

This work presents a closed-form solution to fully-observed variational inference problems, eliminating the need for iterative approximation methods typically used in inference tasks. By leveraging analytical techniques, we derive exact expressions for the variational parameters under full observability, providing both theoretical clarity and computational efficiency. This solution offers improved interpretability and faster convergence in applicable probabilistic models, making it a valuable tool for scalable and accurate inference.

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