Commit 4811bc2c authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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Update 2021JUL01.md

parent 54592b34
......@@ -87,5 +87,10 @@ And we compared three methods:
When $`d >> n`$, Lasso appears superior to the others.
### Panel B - structured ARD priors (in progress).
#### On the high quality data.
Instead of $`\mathbf{w}`\sim\mathcal{N}(0, \text{diag}(\alpha_1,...\alpha_d))$, we assume the hyperparamters have a underlying structure, e.g., $`\mathbf{w}`\sim\mathcal{N}(0, \text{diag}(\exp(\mathbf{u}))$, where $`\mathbf{u}`$ is a Gaussian process $`\mathbf{u}\sim\mathcal{N}(\mathbf{0}, \mathbf{C}_{\Theta})`$.
#### On the high quality data
Instead of $`\mathbf{w}\sim\mathcal{N}(0, \text{diag}(\alpha_1,...\alpha_d))`$, we assume the hyperparamters have a underlying structure, e.g., $`\mathbf{w}\sim\mathcal{N}(0, \text{diag}(\exp(\mathbf{u}))`$, where $`\mathbf{u}`$ is a Gaussian process $`\mathbf{u}\sim\mathcal{N}(\mathbf{0}, \mathbf{C}_{\Theta})`$ such that neighbouring features (i.e., adjoining voxels) share similar sparsity.
#### On the low quality data
### Panel C - structured spike-and-slab priors (in progress).
#### On the high quality data
#### On the low quality data
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