Conditional expectation proof question

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SUMMARY

The discussion centers on proving the property of conditional expectation for two random variables, X1 and X2, using the definition of conditional expectation E(X|Y). The user correctly identifies that E(X1 + X2|Y) can be expressed as E(X1|Y) + E(X2|Y) by substituting X with X1 + X2 and applying the linearity property of expectation. The proof is validated through the manipulation of expected values and functions g(Y), confirming that the approach is accurate and follows the established rules of conditional expectation.

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  • Understanding of conditional expectation in probability theory
  • Familiarity with random variables and their properties
  • Knowledge of the linearity of expectation
  • Basic proficiency in mathematical proofs and functions
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This discussion is beneficial for students and professionals in statistics, data science, and probability theory, particularly those focusing on conditional expectations and their applications in various fields.

oyth94
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Here is a proof question: For two random variables X and Y, we can define E(X|Y) to be the function of Y that satisfies E(Xg(X)) = E(E(X|Y)g(Y)) for any function g. Using this definition show that E(X1 + X2|Y) = E(X1|Y) + E(X2|Y)

So what I did was I plugged into X = X1 + X2
E(E(X1 + X2)|Y)g(Y))
= E(X1g(Y)) + E(X2g(Y))
= E(E(X1|y)g(Y) + E(X2|Y)g(Y))
= E(g(Y) [E(X1|Y) + E(X2|Y)]

am I on the right track? what do I do after that?
 
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