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:
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The quickstart guide.
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A comparison of gradient and hessian algorithms based on particle filters, which in turn are used for conducting parameter inference.
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An example of parameter inference using stochastic optimization.
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An example of parameter inference using Markov chain Monte Carlo.
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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