Research map / Probabilistic and scientific modeling / Variational and generative sampling
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a nonlinear transport map with β=2 instead of a linear map with β=1 (ablation) | SING · Modified Rademacher | graph recovery | Helped |
| Use a nonlinear transport map with β=2 instead of a linear map with β=1 (ablation) | SING · Stochastic volatility, T=6 | graph recovery | Helped |
| Assume Gaussian data and use GLASSO instead of SING with β=2 | Stochastic volatility, T=6 · Stochastic volatility, T=6 | graph recovery | Hurt |
| Use n=2000 samples rather than n=15000 (ablation) | SING, β=2 · Stochastic volatility, T=6; strong edges | recovery of strong edges: n=2000; n=15000 | Mixed |
| Use a sparser transport map matching the graph structure rather than a dense lower-triangular map (ablation) | SING · Gaussian grid graph, p=16 | variance of generalized precision estimates | Helped |
| Oversparsify the transport map to a diagonal map (ablation) | SING · Gaussian grid graph, p=16 | bias of generalized precision estimates | Hurt |
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