Tricky question indeed, stalled for almost an hour, still got the concept and underlying logic at the end

Let's denote the events as follows:
- ( W ) is the event that the rabbit initially in the hat is white.
- ( B ) is the event that the rabbit initially in the hat is black.
- ( P ) is the event that a white rabbit is picked.
Given that initially, the probabilities are:
- P(W) = 0.5
- P(B) = 0.5
The magician adds one more white rabbit to the hat, making the scenarios:
1. If the initial rabbit was white, there are now two white rabbits in the hat.
2. If the initial rabbit was black, there is one white and one black rabbit in the hat.
We are given that a white rabbit is picked. We need to find the probability that the remaining rabbit is white. Using Bayes' theorem, we need to calculate P(W | P) , the probability that the initial rabbit was white given that a white rabbit was picked.
First, we calculate the probabilities of picking a white rabbit under each initial condition:
- If the initial rabbit was white ( W ), the probability of picking a white rabbit is 1 because both rabbits are white.
- If the initial rabbit was black ( B ), the probability of picking a white rabbit is 0.5 because there is one white and one black rabbit in the hat.
Now we use Bayes' theorem:
P(W | P) = (P(P | W) * P(W))/ P(P)
Where:
- P(P | W) is the probability of picking a white rabbit given that the initial rabbit was white.
- P(W) is the prior probability of the initial rabbit being white.
- P(P) is the total probability of picking a white rabbit.
We already know:
- P(P | W) = 1
- P(W) = 0.5
To find P(P) , use the law of total probability:
P(P) = P(P | W) * P(W) + P(P | B) * P(B)
Substituting the known values:
P(P) = (1 * 0.5) + (0.5 * 0.5) = 0.5 + 0.25 = 0.75
Now,
P(W | P) = (1 * 0.5) / 0.75 = 0.5 / 0.75 = 2/3
So, the probability that the remaining rabbit in the hat is also white, given that a white rabbit was picked, is 2/3.