LinearizationImportanceDistribution

class probnum.filtsmooth.LinearizationImportanceDistribution(dynamics_model, linearization_strategy)[source]

Bases: probnum.filtsmooth._particlefiltsmooth._importance_distributions.ImportanceDistribution

Local linearisation importance distribution.

Methods Summary

from_ekf(dynamics_model[, …])

from_ukf(dynamics_model[, spread, priorpar, …])

generate_importance_rv(particle, data, t[, …])

Generate an importance distribution.

log_correction_factor(proposal_state, …)

rtype

float

process_initrv_with_data(initrv, data, t[, …])

Process the initial random variable based on data.

Methods Documentation

classmethod from_ekf(dynamics_model, forward_implementation='classic', backward_implementation='classic')[source]
classmethod from_ukf(dynamics_model, spread=0.0001, priorpar=2.0, special_scale=0.0)[source]
generate_importance_rv(particle, data, t, dt=None, measurement_model=None)[source]

Generate an importance distribution.

log_correction_factor(proposal_state, importance_rv, dynamics_rv, old_weight)
Return type

float

process_initrv_with_data(initrv, data, t, measurement_model=None)[source]

Process the initial random variable based on data.