Probability Review - Expectations

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Homework Help Overview

The discussion revolves around basic probability concepts, specifically focusing on expectations involving independent random variables A and B, which are uniformly distributed on the interval [0,1]. Participants are exploring how to calculate E(min(A,B)), E(|A-B|), and E((A+B)^2).

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • The original poster attempts to calculate E(min(A,B)) using double integrals and presents their solution. They also seek guidance on finding E(|A-B|) and E((A+B)^2), questioning the validity of their approach to the latter. Other participants suggest breaking the integral for E(|A-B|) into two regions based on the relationship between A and B.

Discussion Status

Some participants have provided feedback on the original poster's calculations, with one confirming the correctness of the approach for E(min(A,B)). There is an ongoing exploration of the correct formulation for E((A+B)^2), with a participant pointing out a potential error in the original claim and offering a more general expression. The discussion is active, with participants clarifying assumptions about the independence of A and B.

Contextual Notes

There is a mention of the independence of A and B, which is crucial for the calculations being discussed. The original poster acknowledges a lapse in including this information in their initial query.

spitz
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Homework Statement




I'm trying to review basic probability; haven't looked at it in a couple of years. Am I on the right track here?

A and B are independent random variables, uniform distribution on [0,1]. Find: E(min(A,B))

2. The attempt at a solution

[tex]\displaystyle\int_{0}^{1}\int_{0}^{a}b\,db\,da + \displaystyle\int_{0}^{1} \int_{a}^{1}a\,db\,da[/tex]

[tex]=\displaystyle\int_{0}^{1}\frac{a^2}{2}\,da+\int_{0}^{1}a-a^2\,da[/tex]

[tex]=1/6+3/6-2/6=1/3[/tex]
 
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Looks good to me.
 
Thanks. I also need to find [tex]E(|A-B|)[/tex] and [tex]E((A+B)^2)[/tex]

For the second one: [tex]E((A+B)^2)=E(A)+2E(A)E(B)+E(B)[/tex] and so on ...

Can somebody give me a hint for: [tex]E(|A-B|)[/tex]
 
Last edited:
You'll need to break the integral up into two regions again, A<B and B>A. In one region, |A-B| = A-B, and in the other, |A-B| = B-A.
 
spitz said:
Thanks. I also need to find [tex]E(|A-B|)[/tex] and [tex]E((A+B)^2)[/tex]

For the second one: [tex]E((A+B)^2)=E(A)+2E(A)E(B)+E(B)[/tex] and so on ...

Can somebody give me a hint for: [tex]E(|A-B|)[/tex]

The claim [tex]E((A+B)^2)=E(A)+2E(A)E(B)+E(B)[/tex] is false. For general bivariate (A,B) the correct result is [tex]E (A+B)^2 = E(A^2) + E(B^2) + 2E(AB).[/tex] If A and B happen to be independent (or, at least, uncorrelated) then we have [itex]E(AB) = E(A) \cdot E(B),[/itex] but for general (A,B) this fails. More generally, if A has variance [itex]\sigma_A^2[/itex], B has variance [itex]\sigma_B^2[/itex] and the pair (A,B) has covariance [itex]\sigma_{AB},[/itex] then
[tex]E(A+B)^2 = \mbox{Var}(A+B) + (EA + EB)^2 = \sigma_A^2 + \sigma_B^2 + 2 \sigma_{AB} + (EA + EB)^2.[/tex]

RGV
 
Oh yes, I forgot to square [itex]A[/itex] and [itex]B[/itex]. For this problem they are independent (guess I should have mentioned that). So:
[tex]E((A+B)^2)=E(A^2)+E(B^2)+2E(A)B(A)[/tex]
 

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