Randomized Algorithms (Spring 2010)/Balls and bins

Random Variables and Expectations

Let [math]\displaystyle{ X }[/math] be a discrete random variable.

Definition 1: Two random variables [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are independent if and only if [math]\displaystyle{ \begin{align} \Pr[X=x\wedge Y=y] &= \Pr[X=x]\cdot\Pr[Y=y] \end{align} }[/math] for all values [math]\displaystyle{ x }[/math] and [math]\displaystyle{ y }[/math].

The expectation of [math]\displaystyle{ X }[/math] is defined as follows.

Definition 2: The expectation of a discrete random variable [math]\displaystyle{ X }[/math], denoted by [math]\displaystyle{ \mathbb{E}[X] }[/math], is given by [math]\displaystyle{ \begin{align} \mathbb{E}[X] &= \sum_{x}x\Pr[X=x], \end{align} }[/math] where the summation is over all values [math]\displaystyle{ x }[/math] in the range of [math]\displaystyle{ X }[/math].

Balls-into-bins model

Imagine that [math]\displaystyle{ m }[/math] balls are thrown into [math]\displaystyle{ n }[/math] bins, in such a way that each ball is thrown into a bin which is uniformly and independently chosen from all [math]\displaystyle{ n }[/math] bins. We may ask several questions regarding the distribution of balls in the bins, including:

• the probability that there is no bin with more than one balls (the birthday problem)
• the expected number of balls in each bin (occupancy problem)
• the maximum load of all bins with high probability (occupancy problem)
• the probability that there is no empty bin (coupon collector problem)