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.
FiltSmoothPosterior()	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.

Class Inheritance Diagram