Research map / Probabilistic and scientific modeling / Dynamical systems
Poisson-Gamma dynamical systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PGDS instead of GP-DPFA | GDELT smoothing (top 1,000 features) · GDELT | MRE: 2.951 ± 0.32 -> 2.335 ± 0.19 | Helped |
| Use PGDS instead of GP-DPFA | GDELT smoothing (top 1,000 features) · GDELT | MAE: 9.278 ± 2.01 -> 9.366 ± 2.19 | No clear effect |
| Use PGDS instead of GP-DPFA | GDELT forecasting (top 1,000 features) · GDELT | MRE: 2.207 ± 0.42 -> 2.173 ± 0.41 | No clear effect |
| Use PGDS instead of GP-DPFA | ICEWS smoothing (top 1,000 features) · ICEWS | MRE: 0.877 ± 0.12 -> 0.808 ± 0.11 | Helped |
| Use PGDS instead of GP-DPFA | ICEWS forecasting (top 1,000 features) · ICEWS | MAE: 1.894 ± 0.50 -> 1.788 ± 0.47 | Helped |
| Use PGDS instead of GP-DPFA | SOTU smoothing (top 1,000 features) · SOTU | MRE: 0.238 ± 0.01 -> 0.233 ± 0.01 | No clear effect |
| Use PGDS instead of GP-DPFA | SOTU forecasting (top 1,000 features) · SOTU | MAE: 0.314 ± 0.00 -> 0.323 ± 0.00 | Hurt |
| Use PGDS instead of LDS | DBLP smoothing (top 1,000 features) · DBLP | MRE: 0.405 ± 0.05 -> 0.417 ± 0.03 | Hurt |
| Use PGDS instead of GP-DPFA | DBLP forecasting (top 1,000 features) · DBLP | MAE: 0.715 ± 0.00 -> 0.747 ± 0.01 | Hurt |
| Use GP-DPFA instead of PGDS | NIPS smoothing (top 1,000 features) · NIPS | MRE: 0.415 ± 0.07 -> 0.392 ± 0.07 | Helped |
| Use GP-DPFA instead of PGDS | NIPS forecasting (top 1,000 features) · NIPS | MAE: 62.839 ± 0.37 -> 52.963 ± 0.52 | Helped |
Closest papers by meaning
- Poisson-Randomized Gamma Dynamical Systems (NeurIPS 2019 · similarity 0.92)
- Deep Poisson gamma dynamical systems (NeurIPS 2018 · similarity 0.92)
- A Poisson Gamma Probabilistic Model for Latent Node-Group Memberships in Dynamic Networks (AAAI 2018 · similarity 0.86)
- On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function (NeurIPS 2024 · similarity 0.83)
- Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis (AAAI 2024 · similarity 0.82)
- Fully Heteroscedastic Count Regression with Deep Double Poisson Networks (ICML 2025 · similarity 0.82)
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