Particle filters for Python

Welcome to the pypfilt documentation. This package implements several particle filter methods that can be used for recursive Bayesian estimation and forecasting.

If there is a system or process that can be:

Described (modelled) with mathematical equations; and
Measured repeatedly in some (noisy) way.

Then you can use pypfilt to estimate the state and/or parameters of this system.

_images/lorenz63_forecast_regularised.png — Forecasts for \(x(t)\), \(y(t)\), and \(z(t)\) of the `Lorenz63` system at time \(t=20\).

Depending on your preferred learning style, start with any of the following:

The Getting Started tutorial: Shows how to build a simulation model, generate simulated observations from this model, fit the model to these observations, and generate the example forecasts, above.
The Key Concepts guide: Introduces the various pypfilt components and how they fit together.

Note

This guide is currently incomplete.
The How-to Guides: Demonstrates how to solve particular problems.

Note

These guides are currently incomplete.
The Scenario Settings guide: Explains the settings that can be defined in a scenario file.
The API documentation: Presents the details of each pypfilt component and is likely only of interest if you need to develop your own components.

License

The code is distributed under the terms of the BSD 3-Clause license (see LICENSE), and the documentation is distributed under the terms of the Creative Commons BY-SA 4.0 license.

Citation

If you use pypfilt, please cite our JOSS article and an archived release of the software (see CITATION.cff).

@article{pypfilt,
  author = {Moss, Robert},
  title = {pypfilt: a particle filter for {Python}},
  journal = {Journal of Open Source Software},
  volume = {9},
  issue = {96},
  pages = {6276},
  year = {2024},
  doi = {10.21105/joss.06276},
  note = {Please cite this article and an archived release (see CITATION.cff)},
}

User Documentation