Bayes' Theorem: Dividing P(B|A)*P(A) by P(B) for Accurate Probability

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In Bayes' theorem, P(B|A) * P(A) is divided by P(B) to calculate the posterior probability P(A|B), which represents the likelihood of event A occurring given that event B has occurred. The theorem is grounded in the definition of conditional probability, where P(B|A) is the probability of B given A, and P(A|B) is the probability of A given B. By rearranging the equations for joint probability, it becomes clear that P(A ∩ B) can be expressed in two ways, leading to the formulation of Bayes' theorem. The fraction helps normalize the result, ensuring that the probabilities sum to one. Understanding this relationship is crucial for accurate probability assessments in various applications.
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in the Bayes' theorem, why P(B|A)*P(A) is divided by P(B)? What we want no achieve with the fraction?
 
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Start with the definition of conditional probability:
P(B|A) \equiv \frac{P(A\cap B)}{P(A)}
and similarly,
P(A|B) = \frac{P(A\cap B)}{P(B)}
Solving for P(A\cap B) yields
P(A\cap B) = P(B|A)P(A) = P(A|B)P(B)
Bayes' theorem derives directly from this.
 
I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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