Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Black-box density function estimation using recursive partitioning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DEFER instead of slice sampling | Spectral mixture kernel GP hyperparameter inference · GP time-series regression, test data | negative log-likelihood: 377.66 -> 205.50 | Helped |
| Use DEFER instead of PTMCMC | Spectral mixture kernel GP hyperparameter inference · GP time-series regression, test data | negative log-likelihood: 365.97 -> 205.50 | Helped |
| Use DEFER instead of DNS | Spectral mixture kernel GP hyperparameter inference · GP time-series regression, test data | negative log-likelihood: 236.89 -> 205.50 | Helped |
| Add CR2, which targets affine subspaces through high-mass partitions (ablation) | DEFER · Gravitational-wave parameter inference, 6D | posterior marginal fidelity | Helped |
| Add CR3, which targets neighborhoods around high-mass partitions (ablation) | DEFER · Gravitational-wave parameter inference, 6D | posterior marginal fidelity | No clear effect |
| Add CR2, which targets affine subspaces through high-mass partitions (ablation) | DEFER · Second gravitational-wave parameter inference example, 6D | posterior marginal fidelity | Helped |
| Add CR3, which targets neighborhoods around high-mass partitions (ablation) | DEFER · Second gravitational-wave parameter inference example, 6D | posterior marginal fidelity | Helped |
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