parameter estimation for simple Hawkes (self-exciting) processes
Project description
Welcome to hawkeslib
hawkeslib
started with the ambition of having a simple Python implementation
of plain-vanilla Hawkes (or self-exciting processes), i.e. those
with factorized triggering kernels with exponential decay functions.
The docs contain tutorials, examples and a detailed API reference.
For other examples, see the examples/
folder.
The following models are available:
- Univariate Hawkes Process (with exponential delay)
- Bayesian Univariate Hawkes Process (with exponential delay)
- Poisson Process
- 'Bayesian' Poisson process
Bayesian variants implement methods for sampling from the posterior as well as calculating marginal likelihood (e.g. for Bayesian model comparison).
Installation
Cython
(>=0.28) and numpy
(>=1.14) must be installed prior to the installation.
$ pip install -U Cython numpy hawkeslib
Project details
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