Research map / Probabilistic and scientific modeling / Variational and generative sampling
Nonparanormal Information Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a nonparanormal estimator instead of the Gaussian plug-in estimator | Mutual information estimation with non-Gaussian marginals · Experiment 2, transformed Gaussian data | MSE | Helped |
| Use a nonparanormal estimator instead of the Gaussian plug-in estimator | Mutual information estimation with outliers · Experiment 3 | MSE | Helped |
| Add a small fraction of random outliers | Nonparanormal estimators · Experiment 3 | MSE | Helped |
| Use a rank-based nonparanormal estimator rather than the Gaussian estimator as dependence approaches one | Mutual information estimation · Experiment 4, D = 2 | MSE | Hurt |
| Use Kendall's τ rather than Gaussianization or Spearman's ρ | Nonparanormal mutual information estimation · Experiment 4, strong dependence | MSE | Helped |
| Use nonparanormal estimators rather than the k-nearest-neighbor estimator as sample size grows | Mutual information estimation · Experiment 1, varying n | MSE | Helped |
| Apply nonparanormal estimation when dependencies are strong and Σ is nearly singular | Rank-based nonparanormal estimators · Experiment 4, σ approaching 1 | MSE | Hurt |
| Use rank-based nonparanormal estimation as σ approaches zero | Spearman and Kendall estimators · Experiment 4, σ approaching 0 | MSE | Helped |
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