Research map / Probabilistic and scientific modeling / Variational and generative sampling
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Expxorcist instead of Nonparanormal/GGM on non-Gaussian synthetic data | Expxorcist · Synthetic chain and grid graphs, non-Gaussian, n=500 | ROC (edge recovery) | Helped |
| use Expxorcist instead of Nonparanormal/GGM on Gaussian synthetic data | Expxorcist · Synthetic Gaussian data, n=200 | ROC (edge recovery) | Hurt |
| use Expxorcist instead of Nonparanormal for graph learning | Expxorcist · Futures intraday data | cluster identification (qualitative) | Helped |
| use Expxorcist instead of Nonparanormal/GGM with held-out likelihood tuning | Expxorcist · Futures intraday data | graph sparsity (edges): 325 -> 168 edges | Helped |
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