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The effectiveness of the procedure depends on selecting informative rotations. We develop an efficient PCA-type method that chooses rotations from the leading eigenvectors of a cross-covariance matrix involving the target's score function. Experiments on Bayesian posterior sampling tasks show that performing MFVI in the proposed PCA-rotated coordinate systems substantially improves over standard MFVI, and that the resulting iterative Gaussianization procedure provides accurate flow-like approximations at lower computational cost than conventional normalizing-flow variational approximations.
From: Sifan Liu [view email]
[v1]
Thu, 9 Oct 2025 03:13:44 UTC (288 KB)
[v2]
Thu, 9 Jul 2026 11:39:34 UTC (486 KB)
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