Research map / Probabilistic and scientific modeling / Variational and generative sampling
Estimating Mutual Information for Discrete-Continuous Mixtures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed mixed-variable estimator instead of KSG | Mutual information estimation · Synthetic mixture of continuous and discrete distributions (Experiment I) | mean squared error | Helped |
| Use the proposed mixed-variable estimator instead of noisy KSG | Mutual information estimation · Synthetic mixture of continuous and discrete distributions (Experiment I) | mean squared error | Helped |
| Use the proposed mixed-variable estimator instead of partitioning estimators | Mutual information estimation · Synthetic mixture of continuous and discrete distributions (Experiment I) | mean squared error | Helped |
| Use the proposed mixed-variable estimator instead of competing methods | Mutual information estimation · Discrete X and continuous Y (Experiment II; m=5) | mean squared error | Helped |
| Use the proposed estimator instead of partitioning | Mutual information estimation · Higher-dimensional mixture (Experiments III; 4 and 6 dimensions) | mean squared error | Helped |
| Use the proposed estimator instead of noisy KSG | Mutual information estimation · Higher-dimensional mixture (Experiment III; 6 dimensions) | mean squared error | Mixed |
| Use the proposed estimator instead of adaptive partitioning | Mutual information estimation · Zero-inflated Poissonization (Experiment IV; p=0) | mean squared error | No clear effect |
| Use the proposed estimator instead of competing methods | Mutual information estimation · Zero-inflated Poissonization (Experiment IV; p=15%) | mean squared error | Helped |
| Use the proposed estimator instead of partitioning, noisy KSG, and KSG | Mutual-information feature selection · Synthetic feature selection (20 features; 5 relevant) | ROC curve | Helped |
| Use the proposed estimator instead of competing methods | Gene regulatory network inference · DREAM5 in-silico dataset with simulated dropout | AUROC | Helped |
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