pyrecest 1.1.1

Framework for recursive Bayesian estimation in Python.

PyRecEst

Recursive Bayesian Estimation for Python

PyRecEst is a Python library tailored for recursive Bayesian estimation, compatible with numpy, pytorch, and jax backends.

Features of PyRecEst include:

• Distribution and Densities: Provides tools for handling distributions and densities across Euclidean spaces and manifolds.
• Filters and Trackers: Offers a suite of recursive Bayesian estimators (filters or trackers) for both Euclidean spaces and manifolds. This includes capabilities for:
  • Multi-Target Tracking (MTT)
  • Extended Object Tracking (EOT)
• Evaluation Framework: Contains an evaluation framework to facilitate comparison between different filters.
• Sampling Methods: Includes methods for sampling of the distributions and generating grids.

Usage

Please refer to the test cases for usage examples.

Citation

If you use PyRecEst in your research, please cite:

BibTeX	BibLaTeX
@misc{pfaff_pyrecest_2023, author = {Florian Pfaff}, title = {PyRecEst: Recursive Bayesian Estimation for Python}, year = {2023}, howpublished = {\url{https://github.com/FlorianPfaff/PyRecEst}}, note = {MIT License} }	@software{pfaff_pyrecest_2023_software, author = {Florian Pfaff}, title = {PyRecEst: Recursive Bayesian Estimation for Python}, year = {2023}, url = {https://github.com/FlorianPfaff/PyRecEst}, license = {MIT}, keywords = {Bayesian filtering; manifolds; tracking; Python; NumPy; PyTorch; JAX} }

Credits

• Florian Pfaff ([e-mail])

PyRecEst borrows its structure from libDirectional and follows its code closely for many classes. libDirectional, a project to which I contributed extensively, is available on GitHub. The backend implementations are based on those of geomstats.

License

PyRecEst is licensed under the MIT License.