SPIN Unprocessed October 23, 2026 ai_technology research
Spectral Uncertainty Decomposition for Sparse Bayesian Learning
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arXiv:2610.30207v1 Announce Type: new Abstract: Estimating distributed neural sources from M/EEG measurements is an ill-posed inverse problem where principled uncertainty quantification remains challenging. The usual conditional Gaussian posterior covariance does not explicitly distinguish uncertainty along likelihood-visible source directions from uncertainty that is invisible to the measurements. We propose a spectral framework based on the singular value structure of the prior-preconditioned
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