Show that x and y are independent in this joint distribution

In summary, we can prove that x is independent of w by showing that Pr(x,w) = Pr(x)Pr(w) through the application of Bayes' rule and integration.
  • #1
endeavor
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Given that the joint probability Pr(w,x,y,z) over four variables factorizes as
[itex]Pr(w,x,y,z) = Pr(w) Pr(z|y) Pr(y|x,w)Pr(x)[/itex]
show that x is independent of w by showing that Pr(x,w) = Pr(x)Pr(w).

Attempt: if we simply assume Pr(x,w) = Pr(x)Pr(w), then:
[itex]
\begin{align}
Pr(w,x,y,z) &= Pr(w) Pr(z|y) Pr(y|x,w) Pr(x)\\
&\stackrel{?}= Pr(x,w) Pr(z|y) Pr(y|x,w)\\
&\stackrel{?}= Pr(z|y) Pr(w,x,y)
\end{align}
[/itex]

But can we say the last line equals Pr(w,x,y,z)? I think this problem can be solved by simply applying Bayes' rule several times, but I can't seem to wrap my head around it.
 
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  • #2
What we need to prove is P(w,x) = P(w)P(x). P(w,x) can be computed easily by integrating (or summing) P(w,x,y,z) over all y and z :wink:
 
  • #3
Ah, I see. So we have:
[itex]
\begin{align}
Pr(x,w) &= \iint Pr(w,x,y,z)\,dydz \\
&= \iint Pr(w)Pr(z|y)Pr(y|x,w)Pr(x)\,dydz \\
&= Pr(w)Pr(x) \iint Pr(z|y)Pr(y|x,w)\,dydz \\
&= Pr(w)Pr(x)
\end{align}
[/itex]
 

1. How do you determine if x and y are independent in a joint distribution?

To determine if x and y are independent in a joint distribution, you need to calculate the marginal distributions of both variables and then compare them to the joint distribution. If the product of the marginal distributions is equal to the joint distribution, then x and y are independent.

2. Can x and y be independent in a joint distribution if they are correlated?

Yes, x and y can be independent in a joint distribution even if they are correlated. Independence in a joint distribution refers to the relationship between the two variables, not their correlation. It is possible for two variables to be correlated but still be independent in a joint distribution.

3. What is the significance of determining independence in a joint distribution?

Determining independence in a joint distribution is important because it allows us to simplify the analysis of the relationship between two variables. If two variables are independent, we can make certain assumptions and use simpler statistical methods to analyze their relationship.

4. How does the assumption of independence affect statistical models?

The assumption of independence in a joint distribution can affect statistical models in various ways. If the assumption is not met, the statistical model may not accurately reflect the relationship between the variables and the results may be biased. It is important to carefully consider the assumption of independence when selecting a statistical model.

5. Can independence in a joint distribution change over time?

Yes, independence in a joint distribution can change over time. The relationship between two variables can change due to various factors, and it is important to regularly reassess the assumption of independence in a joint distribution when analyzing data over a long period of time.

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