probnum.filtsmooth

Bayesian filtering and smoothing.

Functions

generate_cd(dynmod, measmod, initrv, times)	Samples true states and observations at pre-determined timesteps “times” for a continuous-discrete model.
generate_dd(dynmod, measmod, initrv, times)	Samples true states and observations at pre-determined timesteps “times” for a continuous-discrete model.

Classes

GaussFiltSmooth(dynamod, measmod, initrv)	Interface for Gaussian filtering and smoothing.
Kalman(dynamod, measmod, initrv)	Kalman filtering and smoothing for continuous-discrete and discrete-discrete state space models.
ExtendedKalman(dynamod, measmod, initrv)	Factory method for Kalman filters.
UnscentedKalman(dynamod, measmod, initrv)	Factory method for Unscented Kalman filters.
UnscentedTransform(ndim[, spread, priorpar, …])	Used for unscented Kalman filter.
ContinuousModel	Interface for time-continuous Markov models of the form dx = f(t, x) dt + l(t, x) dBt.
LinearSDEModel(driftmatrixfct, forcfct, …)	Linear time-continuous Markov models given by the solution of the stochastic differential equation \(dx = [F(t) x(t) + u(t)] dt + L(t) dB(t)\).
LTISDEModel(driftmatrix, force, dispmatrix, …)	Linear time-invariant continuous Markov models of the form dx = [F x(t) + u] dt + L dBt.
DiscreteModel	Abstract interface for state space model components.
DiscreteGaussianModel(dynafct, diffmatfct[, …])	Discrete Gauss-Markov models of the form x_{i+1} = N(g(t_i, x_i), S(t_i)),
DiscreteGaussianLinearModel(dynamatfct, …)	Linear version.
DiscreteGaussianLTIModel(dynamat, forcevec, …)	Discrete Gauss-Markov models of the form x_{i+1} = N(G x_i + z, S),
FiltSmoothPosterior	Posterior Distribution over States after Filtering/Smoothing
KalmanPosterior(locations, state_rvs, …)	Posterior Distribution after (Extended/Unscented) Kalman Filtering/Smoothing

Class Inheritance Diagram