When working with conditional probabilities and Bayes' theorem, always ensure to calculate the total probability of the event first using the law of total probability. Then, use the relevant terms for the numerator and denominator in Bayes' theorem. This method allows you to break down the problem into smaller, manageable steps.
Let \( A \) represent the event that the ball is not black, and \( B_2 \) represent the event that the ball was drawn from Bag-2. We need to find \( P(\neg B_2 \mid A) \), which is the probability that the ball was not drawn from Bag-2, given that it is not black.
We can use Bayes' theorem:
\[ P(\neg B_2 \mid A) = \frac{P(A \mid \neg B_2)P(\neg B_2)}{P(A)}. \]
First, calculate \( P(A) \), the total probability of drawing a non-black ball:
Now, we compute \( P(A) \) using the law of total probability:
\[ P(A) = P(A \mid B_1)P(B_1) + P(A \mid B_2)P(B_2). \]
The probability of drawing from Bag-1 is \( \frac{1}{3} \) (since the die shows a number divisible by 3), and the probability of drawing from Bag-2 is \( \frac{2}{3} \).
Thus:
\[ P(A) = \frac{1}{3} \cdot \frac{2}{5} + \frac{2}{3} \cdot \frac{1}{2} = \frac{2}{15} + \frac{1}{3} = \frac{7}{15}. \]
Next, calculate \( P(A \mid \neg B_2) \), which is the probability of drawing a non-black ball from Bag-1:
\[ P(A \mid \neg B_2) = \frac{2}{5}. \]
Now, apply Bayes' theorem:
\[ P(\neg B_2 \mid A) = \frac{\frac{2}{5} \cdot \frac{1}{3}}{\frac{7}{15}} = \frac{\frac{2}{15}}{\frac{7}{15}} = \frac{2}{7}. \]
Thus, the correct answer is: \[ \frac{2}{7}. \]
Let \( A \) represent the event that the ball is not black, and \( B_2 \) represent the event that the ball was drawn from Bag-2. We need to find \( P(\neg B_2 \mid A) \), which is the probability that the ball was not drawn from Bag-2, given that it is not black.
We can use Bayes' theorem:
\[ P(\neg B_2 \mid A) = \frac{P(A \mid \neg B_2)P(\neg B_2)}{P(A)}. \]
First, calculate \( P(A) \), the total probability of drawing a non-black ball:
Now, we compute \( P(A) \) using the law of total probability:
\[ P(A) = P(A \mid B_1)P(B_1) + P(A \mid B_2)P(B_2). \]
The probability of drawing from Bag-1 is \( \frac{1}{3} \) (since the die shows a number divisible by 3), and the probability of drawing from Bag-2 is \( \frac{2}{3} \).
Thus:
\[ P(A) = \frac{1}{3} \cdot \frac{2}{5} + \frac{2}{3} \cdot \frac{1}{2} = \frac{2}{15} + \frac{1}{3} = \frac{7}{15}. \]
Next, calculate \( P(A \mid \neg B_2) \), which is the probability of drawing a non-black ball from Bag-1:
\[ P(A \mid \neg B_2) = \frac{2}{5}. \]
Now, apply Bayes' theorem:
\[ P(\neg B_2 \mid A) = \frac{\frac{2}{5} \cdot \frac{1}{3}}{\frac{7}{15}} = \frac{\frac{2}{15}}{\frac{7}{15}} = \frac{2}{7}. \]
Thus, the correct answer is:
\[ \frac{2}{7}. \]
The probability of hitting the target by a trained sniper is three times the probability of not hitting the target on a stormy day due to high wind speed. The sniper fired two shots on the target on a stormy day when wind speed was very high. Find the probability that
(i) target is hit.
(ii) at least one shot misses the target. 
Smoking increases the risk of lung problems. A study revealed that 170 in 1000 males who smoke develop lung complications, while 120 out of 1000 females who smoke develop lung related problems. In a colony, 50 people were found to be smokers of which 30 are males. A person is selected at random from these 50 people and tested for lung related problems. Based on the given information answer the following questions: 
(i) What is the probability that selected person is a female?
(ii) If a male person is selected, what is the probability that he will not be suffering from lung problems?
(iii)(a) A person selected at random is detected with lung complications. Find the probability that selected person is a female.
OR
(iii)(b) A person selected at random is not having lung problems. Find the probability that the person is a male.