Analysis of the efficiency of the Ensemble Kalman Filter for Marginal and Joint Posteriors

The ensemble Kalman filter (EnKF) is widely used to sample a probability density function (pdf) generated by a stochastic model conditioned by noisy data. This pdf can be either a joint posterior that describes the evolution of the state of the system in time, conditioned on all the data up to the present, or a particular marginal of this posterior that describes the state at the current time conditioned on all past data. We show that the EnKF collapses in the same way and under even broader conditions as a particle filter when it samples the joint posterior. However, this does not imply that EnKF collapses when it samples the marginal posterior. We we show that a localized and inflated EnKF can efficiently sample this marginal, and argue that the marginal posterior is often the more useful pdf in geophysics. This explains the wide applicability of EnKF in this field. We further investigate the typical tuning of EnKF, in which one attempts to match the mean square error (MSE) to the marginal posterior variance, and show that sampling error may be huge, even if the MSE is moderate.