Research map / Probabilistic and scientific modeling / Dynamical systems
Poisson-Randomized Gamma Dynamical Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Set εθ = 0, allowing gamma latent states to be exactly zero (ablation) | PRGDS · All empirical studies | information gain over BPTF | Helped |
| Use sparse PRGDS (εθ = 0) instead of PGDS | Smoothing studies · Smoothing across the eight studies | information gain over BPTF | Mixed |
| Use PGDS instead of sparse PRGDS (εθ = 0) | Forecasting studies · Forecasting across the eight studies | information gain over BPTF | Mixed |
| Use a dynamic model instead of BPTF | PRGDS, PGDS, and GP-DPFA · All sixteen studies | information gain over BPTF | Helped |
| Use sparse PRGDS instead of the other models | Text, international-event, and neural-spike datasets · Latent structures | temporal localization | Helped |
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- eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling (NeurIPS 2024 · similarity 0.84)
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