FilteringPosterior

class probnum.filtsmooth.FilteringPosterior(locations, state_rvs, transition)

Bases: probnum.filtsmooth.gaussfiltsmooth.kalmanposterior.KalmanPosterior

Filtering posterior.

Parameters

• locations (array_like) – Locations / Times of the discrete-time estimates.

• state_rvs (list of RandomVariable) – Estimated states (in the state-space model view) of the discrete-time estimates.

• transition (Transition) – Dynamics model used as a prior for the filter.

Attributes Summary

locations	Locations / times of the discrete observations
state_rvs	Discrete-time posterior state estimates

Methods Summary

__call__(t)	Evaluate the time-continuous posterior at location t
interpolate(t)	Predict to the present point.
sample([locations, size])	Draw samples from the filtering/smoothing posterior.

Attributes Documentation

locations

Locations / times of the discrete observations

Type

np.ndarray

state_rvs

Discrete-time posterior state estimates

Type

list of RandomVariable

Methods Documentation

__call__(t)

Evaluate the time-continuous posterior at location t

Algorithm: 1. Find closest t_prev and t_next, with t_prev < t < t_next 2. Predict from t_prev to t 3. (if self._with_smoothing=True) Predict from t to t_next 4. (if self._with_smoothing=True) Smooth from t_next to t 5. Return random variable for time t

Parameters

t (float) – Location, or time, at which to evaluate the posterior.

Returns

Estimate of the states at time t.

Return type

RandomVariable

interpolate(t)

Predict to the present point.

sample(locations=None, size=())

Draw samples from the filtering/smoothing posterior.

If nothing is specified, a single sample is drawn (supported on self.locations). If locations are specified, the samples are drawn on those locations. If size is specified, more than a single sample is drawn.

Parameters

• locations (array_like, optional) – Locations on which the samples are wanted. Default is none, which implies that self.location is used.

• size (int or tuple of ints, optional) – Indicates how many samples are drawn. Default is an empty tuple, in which case a single sample is returned.

Returns

Drawn samples. If size has shape (A1, …, Z1), locations have shape (L,), and the state space model has shape (A2, …, Z2), the output has shape (A1, …, Z1, L, A2, …, Z2). For example: size=4, len(locations)=4, dim=3 gives shape (4, 4, 3).

Return type

numpy.ndarray