Bayesian Filtering and Smoothing

Bayesian filtering and smoothing provides a framework for efficient inference in state space models. For non-linear state space components, ProbNum provides linearization techniques that enables Gaussian filtering and smoothing in more complex dynamical systems.

See the notebooks below to learn more about how to use ProbNum’s functionality for Bayesian filtering and smoothing.

You can also interactively try out the ProbNum's Tutorials directly in the browser or by downloading the notebooks from the GitHub repository.