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

AI Thread Summary
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.
xeon123
Messages
90
Reaction score
0
in the Bayes' theorem, why P(B|A)*P(A) is divided by P(B)? What we want no achieve with the fraction?
 
Physics news on Phys.org
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 documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...
Back
Top