# Quiz

The Karhunen-Loève (KL) expansion and the Principal Component Analysis (PCA) are of great importance in shape modelling. Here you can check if you have understood the concepts and why these representations are important.

### Question 1

The KL expansion is a representation of a Gaussian Process and hence probabilistic. Which part makes the model probabilistic?

### Question 2

What are the advantages of the finite, parametric representation? Multiple answers are possible.

### Question 3

When is it not feasible (or not very useful) to obtain a good, finite rank approximation of a Gaussian Process in terms of the leading eigenfunctions?

### Question 4

What are the differences between the PCA and the KL representation? Multiple answers are possible.