Research map / Probabilistic and scientific modeling / Variational and generative sampling
Partially Linear Additive Gaussian Graphical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model confounder effects as a nonparametric, varying nuisance structure and estimate the non-confounded structure with PPL | Gaussian graphical structure recovery · Synthetic structure recovery (p=10, 20, 50, 100) | AUC | Helped |
| Use CON-GGM, which treats the confounder jointly in a GGM | Synthetic structure recovery · Synthetic structure recovery (p=10, 20, 50, 100) | AUC: around 0.5 | Hurt |
| Use PLA-GGM connectivity structure rather than LR-GGM connectivity structure as classifier input | Schizophrenia diagnosis · Schizophrenia diagnosis from fMRI connectivity structure | AUC | Helped |
| Use PLA-GGM connectivity values rather than LR-GGM connectivity values as classifier input | Schizophrenia diagnosis · Schizophrenia diagnosis from fMRI connectivity values | AUC | Helped |
| Vary the user-specified function for identifying confounded observations (ablation) | PLA-GGM · Synthetic structure recovery | AUC | No clear effect |
| Vary the threshold function or g* used for confounder handling (ablation) | PLA-GGM · COBRE fMRI analysis | diagnosis AUC | No clear effect |
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