Massively Parallel Exact Inference for Hawkes Processes
Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes process admits a faster $O(N)$ recurrence, but prior work evaluates this recurrence sequentially, without exploiting parallelization on modern GPUs. We show that the Hawkes process intensity can be expressed as a product of sparse transition matrices admitting a linear-time associative multiply, enabling computation via a parallel prefix scan. This yields a massively parallelizable algorithm for estimation of linear exponential Hawkes processes. Our method reduces the computatio...
| Authors | Ahmer Raza, Hudson Smith |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 5 May 2026 |
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