ParticleFilterPosterior¶
- class probnum.filtsmooth.particle.ParticleFilterPosterior(locations=None, states=None)¶
Bases:
probnum.filtsmooth.TimeSeriesPosterior
Posterior distribution of a particle filter..
Attributes Summary
Methods Summary
__call__
(t)Evaluate the time-continuous posterior at location t
append
(location, state)- rtype
freeze
()- rtype
interpolate
(t)Evaluate the posterior at a measurement-free point.
sample
(rng[, t, size])Draw samples from the filtering/smoothing posterior.
Transform a set of realizations from a base measure into realizations from the posterior.
Attributes Documentation
- frozen¶
- locations¶
- states¶
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.
- Returns
Estimate of the states at time
t
.- Return type
randvars.RandomVariable or _randomvariablelist._RandomVariableList
- interpolate(t)[source]¶
Evaluate the posterior at a measurement-free point.
- Returns
Dense evaluation.
- Return type
randvars.RandomVariable or _randomvariablelist._RandomVariableList
- 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
rng (
Generator
) – Random number generator.t (
Union
[Real
,ndarray
,None
]) – Locations on which the samples are wanted. Default is none, which implies that self.location is used.size (
Union
[Integral
,Iterable
[Integral
],None
]) – 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
np.ndarray