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:
\[ \frac{2}{7}. \]
Based upon the results of regular medical check-ups in a hospital, it was found that out of 1000 people, 700 were very healthy, 200 maintained average health and 100 had a poor health record.
Let \( A_1 \): People with good health,
\( A_2 \): People with average health,
and \( A_3 \): People with poor health.
During a pandemic, the data expressed that the chances of people contracting the disease from category \( A_1, A_2 \) and \( A_3 \) are 25%, 35% and 50%, respectively.
Based upon the above information, answer the following questions:
(i) A person was tested randomly. What is the probability that he/she has contracted the disease?}
(ii) Given that the person has not contracted the disease, what is the probability that the person is from category \( A_2 \)?