# probnum.filtsmooth¶

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.

## Classes¶

 BayesFiltSmooth(dynamics_model, …) Bayesian filtering and smoothing. Kalman(dynamics_model, measurement_model, initrv) Gaussian filtering and smoothing, i.e. Kalman-like filters and smoothers. EKFComponent(non_linear_model) Interface for extended Kalman filtering components. ContinuousEKFComponent(non_linear_model[, …]) Continuous-time extended Kalman filter transition. DiscreteEKFComponent(non_linear_model[, …]) Discrete extended Kalman filter transition. UKFComponent(non_linear_model[, spread, …]) Interface for unscented Kalman filtering components. ContinuousUKFComponent(non_linear_model[, …]) Continuous-time unscented Kalman filter transition. DiscreteUKFComponent(non_linear_model[, …]) Discrete unscented Kalman filter transition. UnscentedTransform(dimension[, spread, …]) Used for unscented Kalman filter. Posterior Distribution over States after Filtering/Smoothing. KalmanPosterior(locations, state_rvs, transition) Interface for posterior distribution after (extended/unscented) Kalman filtering/smoothing. FilteringPosterior(locations, state_rvs, …) Filtering posterior. SmoothingPosterior(locations, state_rvs, …) Smoothing posterior. StoppingCriterion([atol, rtol, maxit]) Stop iteration if absolute and relative tolerance are reached. IteratedDiscreteComponent(component[, stopcrit]) Iterated updates.