Calculating Joint Probability - Get the Answer!

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    Joint Probability
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Discussion Overview

The discussion revolves around the calculation of joint probabilities in a statistical context, specifically focusing on the relationships between different random variables. Participants explore how certain joint probabilities can be shown to be equal and the implications of arbitrary labeling of these variables.

Discussion Character

  • Technical explanation, Mathematical reasoning, Conceptual clarification

Main Points Raised

  • Some participants discuss the expression \(P(X_j=1,X_k=1)=P(X_1=1,X_2=1)\) for \(j \ne k\) and its derivation.
  • Others propose that \(P(X_1=1,X_2=1)=P(X_1=1)+P(X_2=1|X_1=1)\) as a foundational relationship in joint probability.
  • One participant questions how to demonstrate that \(P(X_j, X_i) = P(X_1, X_2) = P(X_1, X_5)\) and expresses uncertainty about the equality of these probabilities.
  • Another participant suggests that the equality arises from the arbitrariness of labeling, asserting that the probabilities remain the same regardless of the indices used.
  • Formal expressions are provided, such as \(P(X_1=1,X_2=1)=P(X_2=1)+P(X_2)P(X_1=1|P(X_2=1)\), to support the argument about the arbitrary nature of indexing.
  • It is noted that \(P(X_2=1|X_1=1)=P(X_2=1|X_5=1)\) and \(P(X_2=1)=P(X_5=1)\), reinforcing the idea of index permutation.

Areas of Agreement / Disagreement

Participants express varying levels of understanding regarding the relationships between the joint probabilities. While some points are clarified, there remains uncertainty about the demonstration of equality between certain probabilities, indicating that the discussion is not fully resolved.

Contextual Notes

Limitations include the dependence on the arbitrary labeling of variables and the need for further clarification on the mathematical steps involved in proving the equalities discussed.

Usagi
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http://img151.imageshack.us/img151/6002/variance.jpg

Just wondering, how did they get the part boxed in blue?

Thanks.
 
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Usagi said:
http://img151.imageshack.us/img151/6002/variance.jpg

Just wondering, how did they get the part boxed in blue?

Thanks.
The part in the box comes from the observation that:

\[P(X_j=1,X_k=1)=P(X_1=1,X_2=1), \ \ j\ne k\],

and that:

\[P(X_1=1,X_2=1)=P(X_1=1)+P(X_2=1|X_1=1)\]

CB
 
Thanks for that, I realized it too however how does one show that $P(X_j, X_i) = P(X_1, X_2) = P(X_1, X_5)$ and so on?

It isn't so obvious to me that $P(X_1, X_2) = P(X_1, X_5)$
 
Usagi said:
Thanks for that, I realized it too however how does one show that $P(X_j, X_i) = P(X_1, X_2) = P(X_1, X_5)$ and so on?

It isn't so obvious to me that $P(X_1, X_2) = P(X_1, X_5)$

The informal explanation is that as the labelling is essentialy arbitary the probabilities are all the same.

More formally:

\[P(X_1=1,X_2=1)=P(X_2=1)+P(X_2)P(X_1=1|P(X_2=1)\]

But \(P(X_2=1|X_1=1)=P(X_2=1|X_5=1)\) and \(P(X_2=1)=P(X_5=1)\). which look better but is still just an observation that the idexing is arbitary and you can just permute indices.

CB
 
Thanks :)
 

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