There are $3$ boys and $x$ girls in a hopital (babies). A mother gives birth to a child but doesn't know it's gender. A midwife picks a random baby, given that she picked a boy, what is the probability it comes from the mother?
I used bayes, but I am struggling to specify (understand/make sense of) the probabilities. Can someone please explain what each of the following probabilities are?
$$P(\text{Boy}\,|\,\text{Mother}),\, P(\text{Boy}),\, P(\text{Mother})$$
With the new information, I made a correction. Also, I added a rather philosophical argument, at the end.
Firstly, the experiment is to take a baby from $4+x$ babies, at random.
$P(B)$ is the probability that a picked baby is a boy. Therefore,
$$P(B)=\frac{1}{2}\frac{4}{4+x}+\frac{1}{2}\frac{3}{4+x}=\frac{7}{2(4+x)}$$
$P(M)$ is the probability that a picked baby belongs to the mother. Therefore
$$P(M)=\frac{1}{4+x}$$
For $P(B|M)$, which is the probability that a picked baby is a boy, when we know that it belongs to the mother, we need to look at it from the mother point of view. She knows that it would be fifty-fifty. Therefore,
$$P(B|M)=\frac{1}{2}$$
Then you just need to use Bayes theorem to calculate
$$P(M|B)=\frac{1}{7}$$
Extension:
Bayesian approach is used when something cannot be measured, using frequentist approach. This problem is a good example. Assume we make the baby, that belongs to the mother, blue and we put a patch on it, so the gender is not observable. If we want to calculate $P(M|B)$, using frequencies, then we have to separate boys and do the following. Take one of the boys at random and show him to the mother. We need to do it many times and Mother would calculate the probability. But, in order to do such a thing, we need to know if the blue baby belongs to boys or not. So, the scenario is not well-defined and therefore, not accurately measurable. That is why we resort to Bayesian approach and calculate things that we can more reliably approximate, like the the $P(M)$, $P(B)$ and combine it with a mere guess of $P(B|M)=0.5$.