To determine the probability that the ball originated from Bag \( B_2 \), given that a white ball was drawn, we apply Bayes' theorem. The events are defined as:
Our objective is to calculate \( P(A_2 \mid W) \), the posterior probability of the ball being from Bag \( B_2 \) given it is white. Bayes' theorem states:
\( P(A_2 \mid W) = \frac{P(W \mid A_2)P(A_2)}{P(W)} \)
Since each bag has an equal probability of selection:
\( P(A_1) = P(A_2) = P(A_3) = \frac{1}{3} \)
The conditional probabilities of drawing a white ball from each bag are:
\( P(W \mid A_1) = \frac{6}{10} = \frac{3}{5} \)
\( P(W \mid A_2) = \frac{4}{10} = \frac{2}{5} \)
\( P(W \mid A_3) = \frac{5}{10} = \frac{1}{2} \)
The total probability of drawing a white ball, \( P(W) \), is computed using the law of total probability:
\( P(W) = P(W \mid A_1)P(A_1) + P(W \mid A_2)P(A_2) + P(W \mid A_3)P(A_3) \)
\( P(W) = \frac{3}{5}\cdot\frac{1}{3} + \frac{2}{5}\cdot\frac{1}{3} + \frac{1}{2}\cdot\frac{1}{3} \)
\( P(W) = \frac{1}{5} + \frac{2}{15} + \frac{1}{6} \)
To sum these fractions, we find a common denominator of 30:
Therefore:
\( P(W) = \frac{6}{30} + \frac{4}{30} + \frac{5}{30} = \frac{15}{30} = \frac{1}{2} \)
Substituting these values into Bayes' theorem for \( P(A_2 \mid W) \):
\( P(A_2 \mid W) = \frac{\frac{2}{5}\cdot\frac{1}{3}}{\frac{1}{2}} \)
\( P(A_2 \mid W) = \frac{\frac{2}{15}}{\frac{1}{2}} = \frac{2}{15} \cdot 2 = \frac{4}{15} \)
The probability that the white ball was drawn from Bag \( B_2 \) is \( \frac{4}{15} \).