A majorization-minimization sequential procedure builds provably convergent upper and lower bounds for moments of scalar and multi-dimensional unnormalized distributions, with an implementation via power diagrams.
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High-dimensional embedding prior improves diffusion-based k-space MRI reconstruction under noise by augmenting representation space.
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A novel sequential method for building upper and lower bounds of moments of distributions
A majorization-minimization sequential procedure builds provably convergent upper and lower bounds for moments of scalar and multi-dimensional unnormalized distributions, with an implementation via power diagrams.