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

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    Conditional Pdf
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Discussion Overview

The discussion revolves around the relationship between conditional probability density functions (PDFs) of two random variables, x1 and x2, where x2 is defined as x1 plus a random variable a. Participants explore the implications of their distributions, particularly focusing on the conditional PDFs f(x2|x1) and f(x1|x2), and whether they can be considered equivalent.

Discussion Character

  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant presents a model where x2 = x1 + a, with both x1 and a following a normal distribution, and claims that the conditional PDFs f(x2|x1) and f(x1|x2) are equal in form but not in distribution.
  • Another participant introduces a simpler model using dice rolls, suggesting that the distribution of x1 given x2 depends on both the distribution of a and the prior distribution of x1, indicating a more complex relationship.
  • A participant expresses appreciation for the clarification that f(x1|x2) can be derived from f(x2|x1) using the prior distribution of x1, but acknowledges confusion regarding their equivalence.
  • Some participants assert that f(x2|x1) and f(x1|x2) do not have the same expression, providing specific mathematical forms to illustrate the differences.

Areas of Agreement / Disagreement

Participants express differing views on the equivalence of the conditional PDFs, with some asserting they are not the same while others initially suggest they are equal in form. The discussion remains unresolved regarding the implications of their differences.

Contextual Notes

There are unresolved assumptions regarding the distributions of x1 and a, as well as the implications of their conditional relationships. The mathematical steps leading to the expressions for the conditional PDFs are not fully explored.

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

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 [itex]f(x_1|x_2)=\frac{f(x_1)f(x_2|x_1)}{f(x_2)}[/itex]. since we already know the "a priori":[itex]f(x_1)[/itex]. And this time [itex]f(x_2|x_1)f(x_1)==f(x_1|x_2)f(x_2)[/itex] for sure.

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

How does this occur?

Thanks in advance.

Hi d9,

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

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

Dear viraltux,
In fact, [itex]f(x_2|x_1) and f(x_1|x_2)[/itex] do not have the same expression, to be more specific,
[itex]\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}[/itex]
Anyway, thanks for your reply.
 
d9dd9dd9d said:
Dear viraltux,
In fact, [itex]f(x_2|x_1) and f(x_1|x_2)[/itex] do not have the same expression, to be more specific,

More even so then :smile:
 

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