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.
Please refer to the test cases for usage examples.
- Florian Pfaff ([email protected])
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.
pyRecEst is licensed under the MIT License.