semvec/pypi-packages huggingface dataset:
hawkeslib 0.2.2
parameter estimation for simple Hawkes (self-exciting) processes
License: MIT
Requires Python: >=2.7.5


# Welcome to `hawkeslib`

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`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](http://hawkeslib.rtfd.io/) 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) and `scipy` must be installed prior to the installation as
they are required for the build.

```
$ pip install -U Cython numpy scipy
$ pip install hawkeslib
```