A Bayesian Characterization of Ensemble Kalman Updates

The update in the Ensemble Kalman Filter (EnKF), called the Ensemble Kalman Update (EnKU), is widely used for Bayesian inference in inverse problems and data assimilation. At each filtering step, it approximates the solution to a likelihood-free Bayesian inversion from an ensemble of particles $(X_i,Y_i)\simπ$ sampled from a joint measure $π$ and an observation $y_\ast\in\mathbb{R}^m$. The posterior $π_{X|Y=y_\ast}$ is approximated by transporting $(X_i,Y_i)$ through an affine map $L^{\mathrm{EnKU}}_{y_\ast}(x,y)$ determined by the Kalman gain. While the EnKU is exact for Gaussian joints $π$ in the mean-field limit, exactness alone does not fix the update: infinitely many affine maps $L_{y_\ast}$ push a Gaussian $π$ to $π_{X|Y=y_\ast}$. This raises a question: which affine map should estimate the posterior? We provide a characterization of the EnKU among all such maps. First, we describe the set $\mathrm{E}^{\mathrm{EnKU}}$ of laws where the EnKU yields exact conditioning, showing it is larger than the Gaussian family. Next, we prove that, except for a small class of highly symmetric distributions in $\mathrm{E}^{\mathrm{EnKU}}$ (including Gaussians), the EnKU is the unique exact affine conditioning map. Finally, we ask for the largest possible set $\mathrm{F}$ where any measure-dependent affine transport could be exact; after characterizing $\mathrm{F}$, we show the EnKU's exactness set is almost maximal: $\mathrm{F}=\mathrm{E}^{\mathrm{EnKU}}\cup\mathrm{S}_{\mathrm{nl-dec}}$, where $\mathrm{S}_{\mathrm{nl-dec}}$ is a small symmetry class. Thus, among affine transports, the EnKU is near-optimal for exact conditioning beyond Gaussians and is the unique affine update achieving exactness for any measure in $\mathrm{F}$ except a subclass of strongly symmetric laws.