Quickstart | Installation | Documentation | Developers
PFJAX is a collection of tools for estimating the parameters of state-space models using particle filtering methods, with JAX as the backend for JIT-compiling models and automatic differentiation.
This will clone the repo into a subfolder pfjax, from where you (i) issue the git clone command and (ii) install the package from source.
git clone https://github.com/mlysy/pfjax
cd pfjax
pip install .A brief introduction to PFJAX.
This is a work in progress! Current modules include:
The quickstart guide.
A comparison of gradient and hessian algorithms based on particle filters, which in turn are used for conducting parameter inference.
An example of parameter inference using stochastic optimization.
An example of parameter inference using Markov chain Monte Carlo.
The API reference documentation.
From within pfjax/tests:
python3 -m unittest -vOr, install tox, then from within pfjax at the command line: tox.
From within pfjax/docs:
# regular build
make html
# clean build incl. repeating cached computations
make clean html