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Spectral Uncertainty Decomposition for Sparse Bayesian Learning
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Bayesian LearningUncertainty QuantificationM/EEG Imaging
2610.30207
Builder Relevance
2h ago70%
Abstract
The paper proposes a spectral framework for uncertainty quantification in estimating distributed neural sources from M/EEG measurements.
Reality Card
Core Claim
The authors provide an exact decomposition of the posterior covariance into data-informed and indeterminacy epistemic components, enhancing uncertainty quantification in M/EEG source imaging.
Method / Result
The framework allows for precise accounting of uncertainty reduction through Bayesian learning.
Limitations
The method's applicability may be limited to linear-Gaussian Bayesian inverse solvers where the posterior covariance is available in closed form.
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