# 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