In Kalman filters, we iterate measurement and motion is often called the measurement update and it’s often called prediction. And the update will use Bayes rule, which is nothing else but a product or a multiplication. In this update, we use total probability which is a convolution or simply an addition. Let’s talk first about the measurement cycle and then the prediction cycle using our great Gaussians for implementing those steps. Suppose you’re localizing another vehicle and you have a prior distribution that looks as follows; it is a very wide Gaussian with the mean over here. And now say we get a measurement that tells us something about the localization vehicle and it comes in like this: it has a mean over here called mu and this example has a much smaller covariance for the measurement. This is an example in our prior we were fairly uncertain about location but the measurement told us quite a bit as to where the vehicle is. Here’s a quiz for you with the new mean of the subsequent Gaussian B over here, over here, or over here.