Is P(A|B) equal to P(A) if and only if P(B|A) is equal to P(B)?

AI Thread Summary
The discussion centers on proving the equivalence of P(A|B) = P(A) and P(B|A) = P(B) in the context of independent probabilities. Participants emphasize the importance of using the correct definitions, particularly distinguishing between intersections and unions in probability formulas. A key point made is that proving one direction of the equivalence can help establish the other by demonstrating that independence is mutual. The conversation also highlights the need for clarity in the proof process to avoid assumptions that do not lead to the desired conclusions. Overall, the thread serves as a collaborative effort to clarify and solidify understanding of independent events in probability theory.
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Independent Probability...

Hello,

Could someone point me in the right direction of how to prove that P(A|B) = P(A) if and only if P(B|A) = P(B)? I think I understand the program and I can't formulate any contradictions, but I'm having difficulty showing this property with a formal proof.

Thanks,
x^2
 
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can you start with the definition

P(A|B) = \frac{P(A \cap B)}{P(B)}
 


P(A|B) = \frac{P(A \cup B)}{P(B)} = P(A)

P(B|A) = \frac{P(A \cup B)}{P(A)} = P(B)

P(B|A) = \frac{P(A \cup B)}{P(B)} = P(A)

P(A|B) = \frac{P(A \cup B)}{P(A)} = \frac{P(A \cup B)}{P(A)} = P(B|A)

I think that is right... Thank you for the hint!
x^2
 


i think you mean intersections, not unions

a better way to show it would be to start with
P(A|B) = P(A)

and use the definition and simplify to show P(B|A) = P(B)
 
Last edited:


lanedance said:
i think you mean intersections, not unions

a better way to show it would be to start with
P(A|B) = P(A|B)

and use the definition and simplify to show P(A) = P(B)

Yes, sorry, you are correct; they should be intersections and not unions.

Thank you for the help!
x^2
 


x^2 said:
P(A|B) = \frac{P(A \cup B)}{P(B)} = P(A)

P(B|A) = \frac{P(A \cup B)}{P(A)} = P(B)

P(B|A) = \frac{P(A \cup B)}{P(B)} = P(A)

P(A|B) = \frac{P(A \cup B)}{P(A)} = \frac{P(A \cup B)}{P(A)} = P(B|A)

I think that is right... Thank you for the hint!
x^2
What is going on in this proof? It looks like you are assuming P(A|B) = P(A) and P(B|A) = P(B) = P(A), and then you don't prove what you set out to prove.

What does P(A|B) = P(A) mean? It means that B's occurring has no effect on the probability of A occurring, i.e., A is independent of B, yes? You need to show that A being independent of B also makes B independent of A. So there is a hint: Do you have a rule that has a different version for independent events?

To prove an equivalence, you can prove the implication both ways: Assume P(A|B) = P(A) and use this to derive P(B|A) = P(B). Then assume P(B|A) = P(B) and use this to derive P(A|B) = P(A).

So the first line in your proof should be

1] P(A|B) = P(A)​

What can you say given also

1] P(A|B) = P(A)
2] P(A ∩ B) = P(B) * P(A|B)​

Remember that you are trying to get to P(B|A) = P(B). So, as a general rule, P(B|A) = ??

Note that A and B are arbitrary events, so the proof in the other direction will be the same.
 


sorry yeah corected post
 
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