These tutorials focus on common PyRecEst tasks. Each page uses the public package-level imports and backend-compatible arrays that are recommended for user code.
- Use a distribution: create distributions, multiply Gaussian factors, and compare the result with the information-form update.
- Write a filter loop: run a Kalman predict/update loop and inspect the posterior state.
- Backend-portable workflows: write a compact Kalman workflow that can be smoke-tested under NumPy, PyTorch, and JAX.
- Robust Kalman updates: use NIS gating and heavy-tailed measurement updates for outlier-prone measurements.
- Run a tracker: initialize a labeled multi-Bernoulli tracker and process cluttered measurements.
- Evaluate a simulation: run a built-in scenario, save evaluation output, and summarize filter performance.
- Getting started covers installation and backend selection.
- API overview maps the main packages.
- Examples lists the executable scripts in the repository.