What is the relationship between conditional PDFs of x1 and x2?

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    Conditional Pdf
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The discussion centers on the relationship between the conditional probability density functions (PDFs) of two variables, x1 and x2, where x2 is defined as x1 plus a normally distributed variable a. Participants clarify that while the conditional PDFs f(x2|x1) and f(x1|x2) may appear to have similar forms, they are not identical due to the underlying distributions of x1 and a. A specific example involving dice rolls illustrates that the distributions can differ significantly based on the conditions set. The conversation emphasizes the subtlety in understanding these conditional relationships and the importance of recognizing the differences in their expressions. Overall, the thread highlights the complexities in conditional probability distributions.
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Hi, all. I happened to think about a problem about conditional PDF:
x_2=x_1+a, x_1 \approx \mathcal{N}(0,1), a \approx \mathcal{N}(0,1)
so the conditional PDF of f(x_2|x_1), f(x_1|x_2) would both be
f(x_2|x_1)=f(x_1|x_2)=\frac{1}{\sqrt{2\pi}}\exp{(-\frac{(x_1-x_2)^2}{2})}
And it is clear that f(x_1) and f(x_2) are not identical, so
f(x_1)f(x_2|x_1) \neq f(x_2)f(x_1|x_2)

How does this occur?

Thanks in advance.
 
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I think P(x1|x2) is more subtle than that. Consider a simpler model. X1 and A are dice rolls. The distribution of x2 = x1+a given x1=3 is uniform 4 to 9. The distribution of X1 given X2 = 9 is uniform 3 to 6, not uniform 3 to 8. I.e. the distribution of X1 given X2 is a consequence of both the distribution of A and the a priori distribution of X1.
 
haruspex said:
I think P(x1|x2) is more subtle than that. Consider a simpler model. X1 and A are dice rolls. The distribution of x2 = x1+a given x1=3 is uniform 4 to 9. The distribution of X1 given X2 = 9 is uniform 3 to 6, not uniform 3 to 8. I.e. the distribution of X1 given X2 is a consequence of both the distribution of A and the a priori distribution of X1.

Dear haruspex, I really appreciate your reply.
Your tolling rice model is really helpful, now I know that f(x_1|x_2)=\frac{f(x_1)f(x_2|x_1)}{f(x_2)}. since we already know the "a priori":f(x_1). And this time f(x_2|x_1)f(x_1)==f(x_1|x_2)f(x_2) for sure.

Thanks again.
 
d9dd9dd9d said:
Hi, all. I happened to think about a problem about conditional PDF:
x_2=x_1+a, x_1 \approx \mathcal{N}(0,1), a \approx \mathcal{N}(0,1)
so the conditional PDF of f(x_2|x_1), f(x_1|x_2) would both be
f(x_2|x_1)=f(x_1|x_2)=\frac{1}{\sqrt{2\pi}}\exp{(-\frac{(x_1-x_2)^2}{2})}
And it is clear that f(x_1) and f(x_2) are not identical, so
f(x_1)f(x_2|x_1) \neq f(x_2)f(x_1|x_2)

How does this occur?

Thanks in advance.

Hi d9,

Actually f(x_2|x_1)≠f(x_1|x_2), they just happen to have the same expression, that's why your confusion.
 
viraltux said:
Hi d9,

Actually f(x_2|x_1)≠f(x_1|x_2), they just happen to have the same expression, that's why your confusion.

Dear viraltux,
In fact, f(x_2|x_1) and f(x_1|x_2) do not have the same expression, to be more specific,
\begin{array}{l}<br /> f({x_2}|{x_1}) = \frac{1}{{\sqrt {2\pi } }}\exp \left( { - \frac{{{{({x_2} - {x_1})}^2}}}{2}} \right),\\<br /> f({x_1}|{x_2}) = \frac{2}{{\sqrt {2\pi } }}\exp \left( { - \frac{{{{({x_2} - {x_1})}^2}}}{2} - \frac{{x_1^2}}{2} + \frac{{x_2^2}}{4}} \right).<br /> \end{array}
Anyway, thanks for your reply.
 
d9dd9dd9d said:
Dear viraltux,
In fact, f(x_2|x_1) and f(x_1|x_2) do not have the same expression, to be more specific,

More even so then :smile:
 
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