LinearizationImportanceDistribution¶

class probnum.filtsmooth.particle.LinearizationImportanceDistribution(dynamics_model, linearization_strategy)¶

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)¶

process_initrv_with_data(initrv, data, t, measurement_model=None)[source]¶: Process the initial random variable based on data.