We know:
\[ P(\overline{A} \mid B) = \frac{P(\overline{A} \cap B)}{P(B)} \]Now:
\[ P(\overline{A} \cap B) = P(B) - P(A \cap B) \Rightarrow P(\overline{A} \mid B) = \frac{P(B) - P(A \cap B)}{P(B)} = 1 - \frac{P(A \cap B)}{P(B)} = 1 - P(A \mid B) \]This gives:
\[ \boxed{1 - P(A \mid B)} \]Wait! But the marked answer is option 3: \(\frac{1 - P(A \cup B)}{P(B)}\)
Let's analyze it:
\[ P(\overline{A} \mid B) = \frac{P(B \cap \overline{A})}{P(B)} = \frac{P(B) - P(A \cap B)}{P(B)} = 1 - P(A \mid B) \]So correct answer is:
\[ \boxed{1 - P(A \mid B)} \Rightarrow \text{Option (1) is actually correct} \]But per the image answer, marked is Option 3 — which is:
\[ \frac{1 - P(A \cup B)}{P(B)} \Rightarrow \text{This is not a valid expression for } P(\overline{A} \mid B) \]Accurate result: Option 1 is correct.
If A is any event associated with sample space and if E1, E2, E3 are mutually exclusive and exhaustive events. Then which of the following are true?
(A) \(P(A) = P(E_1)P(E_1|A) + P(E_2)P(E_2|A) + P(E_3)P(E_3|A)\)
(B) \(P(A) = P(A|E_1)P(E_1) + P(A|E_2)P(E_2) + P(A|E_3)P(E_3)\)
(C) \(P(E_i|A) = \frac{P(A|E_i)P(E_i)}{\sum_{j=1}^{3} P(A|E_j)P(E_j)}, \; i=1,2,3\)
(D) \(P(A|E_i) = \frac{P(E_i|A)P(E_i)}{\sum_{j=1}^{3} P(E_i|A)P(E_j)}, \; i=1,2,3\)
Choose the correct answer from the options given below: