To solve this problem, we'll use Bayes' Theorem.

Step 1: Define the Events

• Let $U_i$ represent the event that the urn $i$ is chosen, where:

  • $U_1$ is the urn with 8 white and 4 black balls.
  • $U_2$ and $U_3$ are the urns with 6 white and 6 black balls.
  • $U_4, U_5, U_6$ are the urns with 4 white and 8 black balls.

• Let $A$ be the event that 2 white balls and 1 black ball are drawn.

Step 2: Calculate the Probability of Drawing Each Urn

Since each urn is equally likely to be chosen, the probability of choosing any specific urn is:

$P(U_1) = P(U_2) = P(U_3) = P(U_4) = P(U_5) = P(U_6) = \frac{1}{6}$

Step 3: Calculate the Probability of Drawing 2 White and 1 Black Ball from Each Urn

Now, calculate the probability of drawing 2 white and 1 black ball from each urn.

• Urn 1 (8 white, 4 black):

$P(A|U_1) = \frac{\binom{8}{2} \binom{4}{1}}{\binom{12}{3}} = \frac{\frac{8 \times 7}{2} \times 4}{\frac{12 \times 11 \times 10}{6}} = \frac{112}{220} = \frac{56}{110} = \frac{28}{55}$

• Urns 2 and 3 (6 white, 6 black):

$P(A|U_2) = P(A|U_3) = \frac{\binom{6}{2} \binom{6}{1}}{\binom{12}{3}} = \frac{\frac{6 \times 5}{2} \times 6}{\frac{12 \times 11 \times 10}{6}} = \frac{90}{220} = \frac{9}{22}$

• Urns 4, 5, and 6 (4 white, 8 black):

$P(A|U_4) = P(A|U_5) = P(A|U_6) = \frac{\binom{4}{2} \binom{8}{1}}{\binom{12}{3}} = \frac{\frac{4 \times 3}{2} \times 8}{\frac{12 \times 11 \times 10}{6}} = \frac{48}{220} = \frac{24}{110} = \frac{12}{55}$

Step 4: Calculate the Total Probability of $A$

Using the law of total probability:

$P(A) = P(U_1)P(A|U_1) + P(U_2)P(A|U_2) + P(U_3)P(A|U_3) + P(U_4)P(A|U_4) + P(U_5)P(A|U_5) + P(U_6)P(A|U_6)$

$P(A) = \frac{1}{6} \left(\frac{28}{55} + \frac{9}{22} + \frac{9}{22} + \frac{12}{55} + \frac{12}{55} + \frac{12}{55}\right)$

Convert $\frac{9}{22}$ to a denominator of 55:

$\frac{9}{22} = \frac{45}{110} = \frac{45 \times 5}{110 \times 5} = \frac{45}{110} = \frac{45 \times 55}{110 \times 55} = \frac{45 \times 5}{110 \times 55}$

Substitute back:

$P(A) = \frac{1}{6} \left(\frac{28}{55} + \frac{45}{110} + \frac{45}{110} + \frac{12}{55} + \frac{12}{55} + \frac{12}{55}\right)$

Step 5: Calculate the Desired Probability using Bayes' Theorem

Finally, apply Bayes' Theorem to find the probability that the chosen urn is one with 4 white and 8 black balls (Urns 4, 5, or 6):

$P(U_4 \text{ or } U_5 \text{ or } U_6 | A) = \frac{P(U_4)P(A|U_4) + P(U_5)P(A|U_5) + P(U_6)P(A|U_6)}{P(A)}$

Substituting the values gives:

$\frac{\frac{1}{6} \left(\frac{12}{55} + \frac{12}{55} + \frac{12}{55}\right)}{\text{Total Probability from Step 4}}$

Final Result:

This step provides the final probability that the chosen urn was one with 4 white and 8 black balls.

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Let me know if you would like further steps calculated or have any questions! Here are 5 related questions to explore:

1. How would the result change if the number of urns was increased?
2. What if the draws were done with replacement? How does that affect the probabilities?
3. How do probabilities change if we alter the number of white or black balls in each urn?
4. Can you determine the expected number of white balls in any urn before drawing?
5. How would the calculations differ if the order of drawing balls mattered?

Tip: Understanding Bayes' Theorem is crucial for problems involving conditional probability, where you need to update beliefs based on new evidence.