Randomized Algorithms (Spring 2010)/Balls and bins: Difference between revisions

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Let <math>X</math> be a discrete '''random variable'''. The expectation of <math>X</math> is defined as follows.
Let <math>X</math> be a discrete '''random variable'''. The expectation of <math>X</math> is defined as follows.


;Definition 1
{|border="1"
:The '''expectation''' of a discrete random variable <math>X</math>, denoted by <math>\mathbb{E}[X]</math>, is given by
|'''Definition 1: '''The '''expectation''' of a discrete random variable <math>X</math>, denoted by <math>\mathbb{E}[X]</math>, is given by
::<math>\begin{align}
::<math>\begin{align}
\mathbb{E}[X] &= \sum_{x}x\Pr[X=x].
\mathbb{E}[X] &= \sum_{x}x\Pr[X=x],
\end{align}</math>
\end{align}</math>
:where the summation is over all values <math>x</math> in the range of <math>X</math>.
|}


=== Linearity of Expectation ===
=== Linearity of Expectation ===

Revision as of 06:46, 15 January 2010

Random Variables and Expectations

Let [math]\displaystyle{ X }[/math] be a discrete random variable. The expectation of [math]\displaystyle{ X }[/math] is defined as follows.

Definition 1: 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].

Linearity of Expectation

Balls-into-bins model

The coupon collector problem

Deviation bounds

Markov's inequality

Chebyshev's inequality

The coupon collector revisited

The [math]\displaystyle{ k }[/math]-Median Problem