Conditional expectation on an indicator

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Homework Help Overview

The discussion revolves around finding the conditional expectations \(\mathbb{E}[X\vert 1_{\{X+Y=0\}}]\) and \(\mathbb{E}[Y\vert 1_{\{X+Y=0\}}]\) for independent Bernoulli random variables \(X\) and \(Y\) with parameter \(p\). Participants are exploring the implications of the condition \(1_{\{X+Y=0\}}\) and its effect on the expectations.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • One participant attempts to show that \(\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] = 0\) using the definition of conditional expectation. Others question the notation of the indicator function and seek clarification on its interpretation.

Discussion Status

Participants are actively discussing the implications of the indicator function and its relationship to the conditional expectations. Some guidance has been provided regarding the interpretation of the indicator, and there is an acknowledgment of the need for a proof rather than speculation.

Contextual Notes

There is a focus on understanding the conditional distribution and its mean, with participants noting that if \(X + Y = 0\), then \(\mathbb{P}[X=0|X+Y=0]=1\). However, the discussion is still open-ended, with no consensus reached on the proof or final conclusions.

IniquiTrance
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Homework Statement



Let X and Y be independent Bernoulli RV's with parameter p. Find,
\mathbb{E}[X\vert 1_{\{X+Y=0\}}] and \mathbb{E}[Y\vert 1_{\{X+Y=0\}}]

Homework Equations

The Attempt at a Solution



I'm trying to show that,

\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] = 0

by,

<br /> \begin{align*}<br /> \mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] &amp;= \frac{\mathbb{E}[(X+Y)1_{\{X+Y=0\}}]}{\mathbb{P}[X+Y=0]} \\<br /> &amp;= \frac{0}{(1-p)^2} \\<br /> &amp;= 0<br /> \end{align*}<br />
 
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IniquiTrance said:
\mathbb{E}[X\vert 1_{\{X+Y=0\}}]
I'm not familiar with the notation for the condition (1_{\{X+Y=0\}}). How does one read it? Can you post a link?
 
haruspex said:
I'm not familiar with the notation for the condition (1_{\{X+Y=0\}}). How does one read it? Can you post a link?

It's the indicator of the event \{X+Y=0\}.

<br /> 1_{\{X+Y=0\}}=\begin{cases}1, \qquad \text{if }X+Y=0; \\ 0, \qquad \text{otherwise.}\end{cases}<br />
 
IniquiTrance said:

Homework Statement



Let X and Y be independent Bernoulli RV's with parameter p. Find,
\mathbb{E}[X\vert 1_{\{X+Y=0\}}] and \mathbb{E}[Y\vert 1_{\{X+Y=0\}}]

Homework Equations

The Attempt at a Solution



I'm trying to show that,

\mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] = 0

by,

<br /> \begin{align*}<br /> \mathbb{E}[X+Y\vert 1_{\{X+Y=0\}}] &amp;= \frac{\mathbb{E}[(X+Y)1_{\{X+Y=0\}}]}{\mathbb{P}[X+Y=0]} \\<br /> &amp;= \frac{0}{(1-p)^2} \\<br /> &amp;= 0<br /> \end{align*}<br />

Basically, you are trying to show that the conditional distribution ##f(k) \equiv P(X= k|X+Y=0), \; k=0,1,2, \ldots## has mean zero; that is, that ##\sum_{k=0}^{\infty} k f(k) = 0##. How do you suppose that could happen?
 
Ray Vickson said:
Basically, you are trying to show that the conditional distribution ##f(k) \equiv P(X= k|X+Y=0), \; k=0,1,2, \ldots## has mean zero; that is, that ##\sum_{k=0}^{\infty} k f(k) = 0##. How do you suppose that could happen?

I think it is possible since both ##X## and ##Y## are Bernoulli, if their sum is 0, then ##\mathbb{P}[X=0|X+Y=0]=1##. Then, ##\sum_{k=0}^1kf(k)=0*1##.
 
IniquiTrance said:
I think it is possible since both ##X## and ##Y## are Bernoulli, if their sum is 0, then ##\mathbb{P}[X=0|X+Y=0]=1##. Then, ##\sum_{k=0}^1kf(k)=0*1##.

Rather than "thinking it is possible" you need to actually prove it. But, at least you are on the right track---that is, your thinking is OK, and just needs to be firmed up. It is not difficult once you see what needs to be established.
 

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