FaDIn: Fast Discretized Inference of Hawkes Processes with General Parametric Kernels¶
Official library for using FaDIn [1]. While exponential kernels are more data efficient and relevant for certain applications where events immediately trigger more events, they are ill-suited for applications where latencies need to be estimated, such as in neuroscience. This work aims to offer an efficient solution to TPP inference using general parametric kernels with finite support. The developed solution consists of a fast L2 gradient-based solver leveraging a discretized version of the events.
Installation¶
To install FaDIn, do:
$ pip install fadin
If you do not have admin privileges on the computer, use the --user flag
with pip. To upgrade, use the --upgrade flag provided by pip.
Dependencies¶
These are the dependencies to use FaDIn:
scipy
numpy (>=1.2)
matplotlib (>=3)
torch (>= 1.12.1)
numba (0.55.2)
Cite¶
[1] Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau. FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. ICML (2023). https://arxiv.org/abs/2210.04635.