Research map / Probabilistic and scientific modeling / Bayesian optimization
A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use L=2 Gaussian mixture instead of L=5 or 10 (ablation) | APF · bimodal SIN | posterior mode recovery: L = 2 vs L = 5, 10 | Hurt |
| increase mixture components from 5 to 10 (ablation) | APF · bimodal SIN | posterior weight accuracy: L = 5 -> L = 10 | Helped |
| increase number of particles K (ablation) | APF · SLAM | log KL-divergence | Helped |
| increase number of samples M beyond a point (ablation) | APF · SLAM | log KL-divergence | No clear effect |
| only update approximate distributions for retained particles (ablation) | APF · SIN | run time: 7 times slower -> 2 times slower | Helped |
| use APF instead of PMMH/PGibbs/PGAS | joint parameter and state estimation · SIN, SLAM, BIRD | accuracy at fixed time | Helped |
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