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The expected value of a random variable is a weighted average of all possible values a random variable can take on, with the weights equal to the probability of taking on that value.

2 votes
1 answer
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Prove E[Y|X] = f(X)

I have a model $Y = f(X) + \epsilon$ where $\epsilon$ is independent of $X$ and $\mathbb{E}[\epsilon]=0, \mathbb{E}\left[\epsilon^2\right]=\sigma^2$. Show that $$ f(X)=\mathbb{E}[Y \mid X] $$ This is …
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Derive E[Y|X] when the joint probability is given

Now, consider joint density of $X, Y$ : $$ f_{X, Y}(x, y)=\left\{\begin{array}{l} \frac{1}{\pi} ; X^2+Y^2<1 \\ 0 ; \text { Otherwise } \end{array}\right. $$ Derive $E(Y \mid X)$. I know how to calcula …
Cabbage Roll's user avatar