ParticleFilterPosterior

class probnum.filtsmooth.particle.ParticleFilterPosterior(locations=None, states=None)

Bases: TimeSeriesPosterior

Posterior distribution of a particle filter..

Attributes Summary

frozen

Whether the posterior is frozen.

locations

Locations of the states of the posterior.

states

States of the posterior.

Methods Summary

__call__(t)

Evaluate the time-continuous posterior at location t

append(location, state)

Append a state to the posterior.

freeze()

Freeze the posterior.

interpolate(t)

Evaluate the posterior at a measurement-free point.

sample(rng[, t, size])

Draw samples from the filtering/smoothing posterior.

transform_base_measure_realizations(...[, t])

Transform base-measure-realizations into posteriors samples.

Attributes Documentation

Parameters:
frozen

Whether the posterior is frozen.

locations

Locations of the states of the posterior.

states

States of the posterior.

Methods Documentation

__call__(t)[source]

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 – Location, or time, at which to evaluate the posterior.

Raises:

ValueError – If time-points are not strictly increasing.

Returns:

Estimate of the states at time t.

Return type:

randvars.RandomVariable or randvars._RandomVariableList

append(location, state)

Append a state to the posterior.

Parameters:
Return type:

None

freeze()

Freeze the posterior.

Return type:

None

interpolate(t)[source]

Evaluate the posterior at a measurement-free point.

Returns:

Dense evaluation.

Return type:

randvars.RandomVariable or randvars._RandomVariableList

Parameters:

t (float | Real | floating) –

sample(rng, t=None, size=())[source]

Draw samples from the filtering/smoothing posterior.

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

Internally, samples from a base measure are drawn and transformed via self.transform_base_measure_realizations.

Parameters:
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:

np.ndarray

transform_base_measure_realizations(base_measure_realizations, t=None)[source]

Transform base-measure-realizations into posteriors samples.

Parameters:
Returns:

Transformed realizations.

Return type:

np.ndarray