Research map / Probabilistic and scientific modeling / Molecules and proteins
Hawkes Process Inference With Missing Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add 5 completely hidden event types to the Hawkes model | Hawkes process, exponential kernel · Chicago gang homicides 1995 next-event prediction | prediction accuracy: 5.3% -> 6.5% | Helped |
| use MCMC with auxiliary variables instead of likelihood weighting | Hawkes posterior sampling · Synthetic hard problems | mean squared relative error vs time | Helped |
| use MCMC instead of likelihood weighting | Hawkes posterior sampling · Synthetic easy problems | mean squared relative error vs time | No clear effect |
| allow move 3 to select previously virtual events as parents (ablation) | MCMC sampler · Synthetic hard problems | mean squared relative error | Helped |
| change kappa from 1 to 4 (ablation) | MCMC sampler · Synthetic small easy problem | mean squared relative error | No clear effect |
Closest papers by meaning
- The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process (NeurIPS 2017 · similarity 0.88)
- Proper Loss Functions for Nonlinear Hawkes Processes (AAAI 2018 · similarity 0.88)
- Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes (NeurIPS 2019 · similarity 0.88)
- Learning Hawkes Processes from a handful of events (NeurIPS 2019 · similarity 0.88)
- Multivariate Hawkes Processes for Large-Scale Inference (AAAI 2017 · similarity 0.88)
- Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes (NeurIPS 2026 · similarity 0.86)
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