Bayesian Filtering and Smoothing.

This package provides different kinds of Bayesian filters and smoothers which estimate the distribution over observed and hidden variables in a sequential model. The two operations differ by what information they use. Filtering considers all observations up to a given point, while smoothing takes the entire set of observations into account.


filter_kalman(observations, locations, F, L, ...)

Estimate a trajectory with a Kalman filter.

smooth_rts(observations, locations, F, L, H, ...)

Estimate a trajectory with a Rauch-Tung-Striebel smoother.



Bayesian filtering and smoothing.

TimeSeriesPosterior([locations, states])

Posterior Distribution over States after time-series algorithms such as filtering/smoothing or solving ODEs.

Class Inheritance Diagram

Inheritance diagram of probnum.filtsmooth.BayesFiltSmooth, probnum.filtsmooth.TimeSeriesPosterior