Probability proof - what formulas are needed here?

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Discussion Overview

The discussion revolves around proofs related to conditional probabilities involving events A and B within the same sample space. Participants explore the implications of certain probability inequalities and the assumptions regarding independence in these contexts.

Discussion Character

  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Post 1 presents two proofs involving conditional probabilities and questions the assumption of independence in the context of these proofs.
  • Post 2 suggests using the definition of conditional probability and clarifies that independence cannot be assumed, while also referencing the standard definition of independence.
  • Post 4 provides a detailed mathematical approach to the first proof, breaking down the steps and showing the derivation of the inequality.
  • Post 3 requests clarification on the steps involved in the proofs, indicating a need for further explanation or guidance.

Areas of Agreement / Disagreement

Participants express differing views on the assumption of independence in the proofs, with some arguing that independence cannot be assumed while others explore the implications of assuming it. The discussion remains unresolved regarding the validity of these assumptions in the context of the proofs.

Contextual Notes

There are limitations regarding the assumptions made about independence and the definitions of conditional probabilities that are not fully explored or resolved in the discussion.

SavvyAA3
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If events A and B are in the same sample space:
  • .
Proove that if P(A I B') > P(A) then P(B I A) < P(B)

(where B' is the Probability of A given not B)


  • .
Proove that if P(A I B) = P(A) then P(B I A) = P(B)

do we assume independence here so that P(A I B) = [P(A)*P(B)]/ P(B) = P(A) and state that since P(A n B) = P(B n A) that P(B I A) = [P(B)*P(A)] / P(A) = P(B) or is it wrong to assume independence here?
 
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For the second proof use the fact that P(A|B)=P(A&B)/P(B) and similarly for the other one. You can't assume independence, but it is easy to see that they are using the usual definition of independence P(A&B)=P(A)P(B).
 
Please could you show me the steps you would take
 
SavvyAA3 said:
If events A and B are in the same sample space:
  • .
Proove that if P(A I B') > P(A) then P(B I A) < P(B)

(where B' is the Probability of A given not B)

...

assume
[tex]P(A|B')>P(A)[/tex]
then

[tex]\frac{P(A\cap B')}{P(B')}>P(A)[/tex]

[tex]\frac{P(B'|A)P(A)}{P(B')}>P(A)[/tex]

[tex]\frac{P(B'|A)}{P(B')}>1[/tex]

[tex]P(B'|A)>P(B')[/tex]

[tex]1-P(B'|A)<1-P(B')[/tex]

[tex]P(B|A)<P(B)[/tex]

the other one isn't much different
 
Thanks soo much!
 

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