State prediction ($\mathbb{E}[x_{t+1}\mid y_1,\dots ,y_t]$)

State prediction estimates a future state of a dynamical system using observations already available. A state-space model supplies the transition law. Unlike causal state filtering at the next time, the prediction has not yet incorporated that next observation; a Kalman prediction step therefore includes uncertainty from new process noise.