Concept:
This is an application of Bayes’ Theorem, which allows us to update probabilities based on new information.
If \(A_1, A_2, A_3\) are mutually exclusive and exhaustive events and \(B\) is another event, then:
\[
P(A_i|B) = \frac{P(A_i)\,P(B|A_i)}{\sum_{j} P(A_j)\,P(B|A_j)}
\]
Step 1: Define the events clearly.
Let:
\[
A_1 = \text{opponent is level 1},
A_2 = \text{opponent is level 2},
A_3 = \text{opponent is level 3}
\]
\[
B = \text{you win the game}
\]
From the question:
\[
P(A_1)=0.5, P(A_2)=0.25, P(A_3)=0.25
\]
Winning probabilities:
\[
P(B|A_1)=0.3, P(B|A_2)=0.4, P(B|A_3)=0.5
\]
Step 2: Find the total probability of winning \(P(B)\).
Using the law of total probability:
\[
P(B) = P(A_1)P(B|A_1) + P(A_2)P(B|A_2) + P(A_3)P(B|A_3)
\]
Substituting values:
\[
P(B) = (0.5)(0.3) + (0.25)(0.4) + (0.25)(0.5)
\]
Now compute each term separately:
\[
(0.5)(0.3) = 0.15
\]
\[
(0.25)(0.4) = 0.10
\]
\[
(0.25)(0.5) = 0.125
\]
Adding:
\[
P(B) = 0.15 + 0.10 + 0.125 = 0.375
\]
Step 3: Apply Bayes’ Theorem to find \(P(A_1|B)\).
\[
P(A_1|B) = \frac{P(A_1)P(B|A_1)}{P(B)}
\]
Substitute:
\[
P(A_1|B) = \frac{(0.5)(0.3)}{0.375}
\]
Compute numerator:
\[
(0.5)(0.3) = 0.15
\]
Thus:
\[
P(A_1|B) = \frac{0.15}{0.375}
\]
Step 4: Simplify the fraction carefully.
Multiply numerator and denominator by 1000 to remove decimals:
\[
\frac{150}{375}
\]
Divide both by 75:
\[
= \frac{2}{5} = 0.4
\]
Step 5: Interpret the result.
This means that given that you won the game, the probability that your opponent was a level 1 player is \(0.4\).