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Ensemble Kalman Filter Log Marginal Likelihood.

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

This method estimates (logarithm of) the marginal likelihood of the observation at step k, Y_k, given previous observations from steps 1 to k-1, Y_{1:k}. In other words, Log[p(Y_k | Y_{1:k})]. This function's approximation to p(Y_k | Y_{1:k}) is correct under a Linear Gaussian state space model assumption, as ensemble size --> infinity.

state Instance of EnsembleKalmanFilterState at step k, conditioned on previous observations Y_{1:k}. Typically this is the output of ensemble_kalman_filter_predict.
observation Tensor representing the observation at step k.
observation_fn callable returning an instance of tfd.MultivariateNormalLinearOperator along with an extra information to be returned in the EnsembleKalmanFilterState.
seed PRNG seed; see tfp.random.sanitize_seed for details.
name Python str name for ops created by this method. Default value: None (i.e., 'ensemble_kalman_filter_log_marginal_likelihood').

log_marginal_likelihood Tensor with same dtype as state.