Summary of Flexible Parametric Inference For Space-time Hawkes Processes, by Emilia Siviero et al.
Flexible Parametric Inference for Space-Time Hawkes Processes
by Emilia Siviero, Guillaume Staerman, Stephan Clémençon, Thomas Moreau
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper develops a fast and flexible parametric inference technique for recovering the parameters of kernel functions in space-time Hawkes processes. This technique combines kernels with finite support, domain discretization, and precomputations to facilitate efficient and accurate parameter estimation. The approach is demonstrated through numerical experiments on synthetic and real spatio-temporal data, showcasing its relevance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to analyze data that shows how events are connected in space and time. It’s like looking at a map of earthquakes or traffic patterns. The method uses special functions called kernel functions to understand when and where these events happen. The researchers developed an algorithm to find the right values for these kernel functions, which is important because it helps us make predictions about future events. |
Keywords
» Artificial intelligence » Inference
