In Kalma Filter then, we’re going to build a two-dimensional estimate. One for the location and one for the velocity denoted x. But it also can be 0, it can be negative, or it can be positive. If initially, I know my location but not my velocity, then I represented Gaussian as elongated around the correct location. But really, really broad in the space of velocities. Now let’s look at the predictions step. In the prediction step, I don’t know my velocity. So, I can’t possibly predict for location I want to saw. But miraculously, they have some interesting correlation. So this for a second, just pick a point on this distribution over here. Let me assume my velocity is 0. Of course in practice I don’t know the velocity but let me assume for a moment the velocity is 0. Where would my posture be after the prediction? Well, we know we started location one, the velocity of 0, so my location would likely be here. Now let’s change my belief of the velocity and pick a different one. Let’s say the velocity is 1. Where would my prediction be one timestep later, starting at location one, and velocity 1? I give you three choices. Here, here, or here? Please pick the one that makes most sense.