In this chapter we extend our discussion of the previous chapter to model dynamical systems with continuous state-spaces. We present statistical formulations to model and analyze noisy trajectories that evolve in a continuous state space whose output is corrupted by noise. In particular, we place special emphasis on linear Gaussian state-space models and, within this context, present Kalman filtering theory. The theory presented herein lends itself to the exploration of tracking algorithms explored in the chapter and in an end-of-chapter project.