You organized a group gift for a friend’s birthday, and you advanced the money on behalf of the other participants in the gift. It is now time to get reimbursed by each of them.
To do this, you must call the other contributors one by one on the phone - but your friends are a bit stingy, and try to avoid your call, so that you only have a probability of reaching the person you’re calling...
We assume that the calls are independent trials (the participants don’t warn each other), and we let denote the random variable representing the number of gift participants you manage to reach.
- What is the distribution of ?
The next day, you call back, under the same conditions, the most stingy participants who did not answer the day before. Let denote the random variable representing the number of people you manage to reach during this second attempt.
For , determine for .
Prove that follows a binomial distribution, and determine its parameter.
Determine the expectation and the variance of .
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Because there are independant calls with probability of success, follows a binomial distribution :
In the second round, the calls are still independant and each with probability to succeed, but this time, if , there are only calls. So the conditional probability is again a binomial distribution :
Disclaimer : there is probably a better way than the brute force method I did, but I can’t find the right interpretation: when , then we have to have . This means that :
But these joint probabilities can be expressed using the conditional probabilities which were computed in the previous question, and using the first question:
and a few terms do not depend on , so they can be extracted from the sum:
Here there is a nice combinatorial identity:
which yields:
This sum is very close to a binomial type of expression. We make a appear:
By letting , we have that , we just obtained:
This is again a binomial distribution .The expected value of is , while the variance is .
Comme il y a appels indépendants, chacun avec une probabilité de succès, suit une loi binomiale :
Lors du deuxième appel, les appels restent indépendants, chacun avec une probabilité de succès, mais cette fois, si , il ne reste que appels à passer. La probabilité conditionnelle suit donc à nouveau une loi binomiale :
Attention : il y a probablement beaucoup mieux que cette méthode "force brute", mais je n’ai toujours pas trouvé la bonne interprétation : Lorsque , il faut nécessairement que . Cela signifie que :
Or ces probabilités jointes peuvent s’exprimer à l’aide des probabilités conditionnelles calculées à la question précédente, ainsi que du résultat de la première question :
et certains termes ne dépendent pas de , donc on peut les sortir de la somme :
On dispose ici d’une identité combinatoire remarquable :
ce qui donne :
Cette somme fait beaucoup penser à un binôme de Newton, mais la puissance n’est pas la bonne. Faisons apparaître un :
En posant , on a , d’où l’on obtient :
Il s’agit à nouveau d’une loi binomiale .L’espérance de vaut , tandis que la variance vaut .
