# 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.

## Functions¶

 effective_number_of_events(categ_rv) Approximate effective number of events in the support of a categorical random variable.

## 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. TimeSeriesPosterior(locations, states) Posterior Distribution over States after time-series algorithms such as filtering/smoothing or solving ODEs. KalmanPosterior(locations, states, transition) Posterior distribution after approximate Gaussian filtering and smoothing. FilteringPosterior(locations, states, transition) Filtering posterior. SmoothingPosterior(locations, states, …) Smoothing posterior. StoppingCriterion([atol, rtol, maxit]) Stop iteration if absolute and relative tolerance are reached. IteratedDiscreteComponent(component[, stopcrit]) Iterated updates. ParticleFilter(dynamics_model, …[, …]) Particle filter (PF). ParticleFilterPosterior(locations, states) Posterior distribution of a particle filter..