Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Multi-resolution Multi-task Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a composite likelihood with information-theoretic weight correction instead of a product likelihood | MR-GP · Synthetic dependent observation processes | posterior variance calibration | Helped |
| Assume a product likelihood for dependent observation processes | Gaussian process model · Synthetic dependent observation processes | posterior uncertainty calibration | Hurt |
| Use MR-DGP to learn mappings between observation processes | Air pollution estimation across London · London NO2 | RMSE | Helped |
| Use MR-DGP rather than the alternative approaches to model scaling biases | Air pollution estimation models · Synthetic biased observation processes | RMSE | Helped |
| Use a tree-structured mixture of experts instead of a cascade (ablation) | MR-DGP · MR-DGP comparison experiments | predictive performance | Helped |
| Use aggregation-region integrals instead of center points | Multi-resolution GP models · Inter-task multi-resolution PM25 prediction | missing-data predictive MSE | Helped |
| Constrain GPRN weights to be positive using an exponential transform (ablation) | MR-GPRN · Multi-resolution prediction | predictive performance | Helped |
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