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Found 3 papers, 1 papers with code
Dynamic Online Ensembles of Basis Expansions
Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models.
Bayesian Reconstruction of Fourier Pairs
Our aim is to address the lack of a principled treatment of data acquired indistinctly in the temporal and frequency domains in a way that is robust to missing or noisy observations, and that at the same time models uncertainty effectively.
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