Conditional Probability and Bayes' Formula Questions

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Conditional probability is defined as P(A|B) = P(A ∩ B) / P(B), while Bayes' Theorem allows for the reversal of conditions, expressed as P(A|B) = (P(B|A) * P(A)) / P(B). Bayes' Theorem is applicable to any conditional probability problem where P(B) is greater than zero. It serves as a versatile tool in probability theory, enabling the calculation of probabilities in various contexts. Understanding these concepts is essential for effectively applying probability in real-world scenarios.
sampahmel
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Dear all,

Please clarify the following:

1.) The difference of conditional probability and Bayes' formula.

2.) Is Bayes' formula a "all weather condition" formula for all conditional probabilities problem?


Thank you,

S
 
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Can anyone please answer to the above question?
 
1) Conditional probability is \mathbb{P}(A|B):=\frac{\mathbb{P}(A \cap B)}{\mathbb{P}(B)}.

Bayes Theorem is used to swap the condition around,
\mathbb{P}(A|B):=\frac{\mathbb{P}(B|A) \mathbb{P}(A)}{\mathbb{P}(B)}

2) You can use Bayes formula for any conditional probabilities such that \mathbb{P}(B)>0
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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