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Analysis of the Gibbs Sampler for hierarchical inverse problems

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Academic year: 2020

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Figure

Figure 1. Left: true solution (dashed black) and noisy data (blue continuous). Middle and right: truesolution (dashed black), sample mean (red continuous), and(middle) and NCA (right)
Figure 2. CA:N = 32 δ-chains (top) and kernel density estimates of the posterior on δ (bottom) for dimensions, 512, and 8192 left to right.In dashed red in the density plots is the density estimate using MA,considered as the gold standard.
Figure 4. Autocorrelation functions of δ-chain, dimensions 32 (black), 512 (red), and 8192 (blue); left forMA, middle for CA, and right for NCA.
Figure 5. Left: true solution (dashed black) and discrete blurred noisy data (blue asterisks)
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