Proof of expected value expression

AI Thread Summary
The discussion focuses on deriving an alternative expression for the expected value of a non-negative discrete random variable X. The standard formula for expected value is E[X] = (sum) x*p(x). The user seeks guidance on proving that E[X] can also be expressed as E[X] = (sum: i from 1 to infinity) P(X>=i). A suggested approach involves using the relationship P(X>=i) = (sum(j=i,inf) P(X=j)) and interchanging the summation indices to simplify the expression. This method ultimately leads to the desired proof of the expected value expression.
grimster
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The usual expression of the expected value of X is:
E[X] = (sum) x*p(x)

i'm supposed to show that, for X a random non-negative discrete random(stochastic) variable, we have that:
E[X]=(sum: i from 1 to infinity) P(X>=i)

i have absolutely no idea how to do this. does anyone want to push me in the right direction?
 
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P(X>=i)=sum(j=i,inf) P(X=j). Plug this into your sum over i, interchange i and j and you will get what you want, since the sum over i will simply be j.
 
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