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

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SUMMARY

The discussion centers on proving the equivalence of conditional probabilities, specifically that P(A|B) = P(A) if and only if P(B|A) = P(B). Participants clarify that this relationship indicates the independence of events A and B. The proof involves starting with the definition of conditional probability and simplifying to demonstrate the independence in both directions. Key definitions and corrections regarding intersections versus unions are emphasized throughout the conversation.

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  • Understanding of conditional probability, specifically P(A|B) and P(B|A)
  • Familiarity with the concept of independent events in probability theory
  • Knowledge of set operations, particularly intersections and unions
  • Ability to manipulate and simplify probability expressions and equations
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  • Study the formal definitions of conditional probability and independence in probability theory
  • Learn how to derive implications in probability proofs, focusing on independence
  • Explore examples of independent events and their properties in real-world scenarios
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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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