science forum beginner
Joined: 03 Feb 2005
|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,
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.