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Jason Simons
science forum beginner

Joined: 03 Feb 2005
Posts: 8 Posted: Wed May 17, 2006 6:41 pm    Post subject: Understanding Proof of Markov Inequality The Markov inequality says the following:

If X is a non-negative random variable and a is a positive constant,
then

P( X >= a ) <= E[X]/a

I tried some examples and can see that, for my examples, this is true.

The following was given in class as a proof of this.

Proof: Let I be the indicator random variable that is 1 when X >= a and
0 otherwise. Then

I <= X/a, so

E[I] = P( I = 1 ) = P( X >= a ) <= E[ X/a ] = E[X]/a

My question concerns the motivation behind the first move. Why should I
set up an indicator variable "I" equal to 1 when X >= a and zero
otherwise. We're concerned about the "probability" of X >= a not the
value of X itself. I never would have come up with this idea (at least
at my current level of understanding). It's apparent that there is some
relationship between X >= a and the theorem, but I don't see it.

Anyone help?

thanks,

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