NonlinearSSM._gaussian_update#

NonlinearSSM._gaussian_update(y_row, predicted_obs, innovation_cov, cross_cov, mean, cov)[source]#

One Gaussian measurement update from moment inputs.

Return type:: tuple[NDArray[double], NDArray[double], float]
Returns:: (filtered_mean, filtered_cov, log_contribution).
Raises:: NumericalError – If the innovation covariance degenerates.

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Pre-releases are published to PyPI and announced on GitHub Releases. Pin the exact version in anything that must not move.

pip install --pre cultivars==1.0.0a4

© 2026, Nikhil Sunder. Released under the MIT License. cultivars is an independent open-source project and is not affiliated with the Federal Reserve Bank of St. Louis; FRED® is a registered trademark of the Federal Reserve Bank of St. Louis.

cultivars 1.0.0a4 · updated 2026-10-13 · built with Sphinx 9.1.0 and the PyData Sphinx Theme