  The use of random samples to approximate properties of geometric
configurations has been an influential idea for both combinatorial and
algorithmic purposes. This chapter considers two related
notions---$\epsilon$-approximations and $\epsilon$-nets---that capture the most
important quantitative properties that one would expect from a random sample
with respect to an underlying geometric configuration.
