# Quiz

We have seen that Gaussian Processes are not only a nice theoretical concept, but lead to practical computer implementations in terms of the multivariate normal distribution, thanks to the marginalisation property. In this quiz we further explore this property as well as the connection between the Gaussian Process and the multivariate normal distribution.

### Question 1

Why is marginalisation special in the case of normal distributions? What is the problem when computing the marginal with arbitrary distributions?

### Question 2

If you marginalise out variables of a multivariate normal distribution, how are their values reflected in the marginal distribution?

### Question 3

Why is it better to model continuous variations of shapes instead of discrete ones?

### Question 4

We saw that the marginalisation property makes Gaussian Processes practical. However, if we choose too many points for discretisation, we still run into a problem. Where do you think the bottleneck is?