Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Spike and slab variational Bayes for high dimensional logistic regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Laplace rather than Gaussian prior slabs in the spike-and-slab VB method (ablation) | Mean-field spike-and-slab VB · Gaussian design, n=250, p=500 | FDR: 0.84 ± 0.04 -> 0.03 ± 0.10 | Helped |
| Use Laplace rather than Gaussian prior slabs in the spike-and-slab VB method (ablation) | Mean-field spike-and-slab VB · Gaussian design, n=250, p=500 | ℓ2-error: 3.34 ± 0.46 -> 0.57 ± 0.37 | Helped |
| Use Laplace rather than Gaussian prior slabs in the spike-and-slab VB method (ablation) | Mean-field spike-and-slab VB · Gaussian design, n=250, p=500 | MSPE: 0.25 ± 0.03 -> 0.04 ± 0.02 | Helped |
| Use Laplace rather than Gaussian prior slabs in the spike-and-slab VB method (ablation) | Mean-field spike-and-slab VB · Gaussian design, n=250, p=500 | Time: 0.98 ± 0.51 -> 12.10 ± 0.49 | Hurt |
| Use Laplace rather than Gaussian prior slabs (ablation) | Mean-field spike-and-slab VB · Additional simulation tests | ℓ2-error | Mixed |
| Use Laplace rather than Gaussian prior slabs (ablation) | Mean-field spike-and-slab VB · Additional simulation tests | Time | Hurt |
| Increase the slab scale hyperparameter λ (ablation) | VB (Lap) · Gaussian design, n=250, p=500 | Estimation performance | Hurt |
Closest papers by meaning
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