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Ensemble Kalman Filter Update.

The Ensemble Kalman Filter is a Monte Carlo version of the traditional Kalman Filter.

This method is the 'update' equation associated with the Ensemble Kalman Filter. In expectation, the ensemble covariance will match that of the true posterior (under a Linear Gaussian State Space Model).

state Instance of EnsembleKalmanFilterState.
observation Tensor representing the observation for this timestep.
observation_fn callable returning an instance of tfd.MultivariateNormalLinearOperator along with an extra information to be returned in the EnsembleKalmanFilterState.
damping Floating-point Tensor representing how much to damp the update by. Used to mitigate filter divergence. Default value: 1.
seed Python int seed for random ops.
name Python str name for ops created by this method. Default value: None (i.e., 'ensemble_kalman_filter_update').

next_state EnsembleKalmanFilterState representing particles at next timestep, after applying Kalman update equations.