Causal state filtering ($\mathbb{E}[x_t\mid y_1,\dots ,y_t]$)

Causal state filtering estimates the current state of a dynamical system from observations up to the current time. Under quadratic loss, the conditional expectation is an optimal estimate. A Kalman filter obtains this estimate recursively for a linear Gaussian state-space model, without using future observations.