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

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== Random Variables and Expectations ==
== Random Variables and Expectations ==
What do we mean by the sentence "<math>X</math> is a '''random variable'''"? I guess most os us just imagine that <math>X</math> is like a random dice whose value is ranging over a set <math>R</math> of values, such that for each <math>x\in R</math>, <math>X=x</math> with some specific probability <math>p(x)</math>, so we say that "<math>X</math> is distributed over <math>R</math> with probability distribution <math>p</math>".
What do we mean by the sentence "<math>X</math> is a random variable"? I guess most os us just imagine that <math>X</math> is like a random dice whose value is ranging over a set <math>R</math> of values, and <math>X</math> is randomly equal to some element of <math>R</math>, like we saying that "<math>X</math> is distributed over <math>R</math>".


While this intuition is quite convenient for us and works most of times, we need a more formal definition of '''random variables'''


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

Revision as of 06:22, 15 January 2010

Random Variables and Expectations

What do we mean by the sentence "[math]\displaystyle{ X }[/math] is a random variable"? I guess most os us just imagine that [math]\displaystyle{ X }[/math] is like a random dice whose value is ranging over a set [math]\displaystyle{ R }[/math] of values, and [math]\displaystyle{ X }[/math] is randomly equal to some element of [math]\displaystyle{ R }[/math], like we saying that "[math]\displaystyle{ X }[/math] is distributed over [math]\displaystyle{ R }[/math]".

While this intuition is quite convenient for us and works most of times, we need a more formal definition of random variables

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