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.

Credits

• Florian Pfaff (pfaff@ias.uni-stuttgart.de)

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.