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.
merge_regression_problems(…)	Make a new regression problem out of two other regression problems.

Classes

BayesFiltSmooth(prior_process)	Bayesian filtering and smoothing.
Kalman(prior_process)	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(prior_process, …[, …])	Particle filter (PF).
ParticleFilterPosterior(locations, states)	Posterior distribution of a particle filter..
ImportanceDistribution(dynamics_model)	Importance distributions used in particle filtering.
BootstrapImportanceDistribution(dynamics_model)	Bootstrap particle filter importance distribution.
LinearizationImportanceDistribution(…)	Local linearisation importance distribution.

Class Inheritance Diagram