How can I solve this variation of the famous birthday problem?

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In summary, the conversation discusses the attempt to solve a variation of the famous birthday problem, which asks for the probability of at least k people sharing a birthday out of a group of n people. The problem can also be described as the probability of at least one bin containing k or more items when randomly distributing m items into n bins with a probability of 1/m for each item to be put into a bin. The individual has searched for ways to solve this problem but has not found much success. One approach considered is using a computer program to calculate all possible ways of distributing the items, but this becomes infeasible for large values of n. A more efficient solution may involve a probabilistic approach, but the individual is unsure of how to
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tjaeger
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So, I've been trying to solve a variation of the famous birthday problem. The problem is a more generalized version and goes like this: given n people, what is the probability at least k people share a birthday? You could also describe the problem as such: given m items randomly distributed into n bins (a probability 1/m of an item being put into a bin) what is the probability that at least one bin has at least k items. I've been searching the internet for ways to try to solve this problem, but I haven't had much luck. Is there a name for this type of problem or does anyone have any insights on this problem?

My first intuition on how to solve this problem would be to use a computer program to calculate all the ways the items can be distributed for n items and finding out how many of the ways have bins have bins with k or more items.

For instance, if n = 5 the items can be distributed as follows:
1+1+1+1+1, 2+1+1+1, 3+1+1, 2+2+1, 3+2, 4+1, 5

Then, for each of these partitions find out how many ways they can be spread out over m bins. However, this becomes infeasible quickly because for n=100 there are 190569292 possible partitions and for n=1000 there are 24061467864032622473692149727991 partitions.
 
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A more efficient way to solve this problem might be to use a probabilistic approach and calculate the probability of at least one bin having k items. From my understanding, this would involve finding the probability that a bin has k items, and then using this to calculate the probability of multiple bins each having k items. However, I'm not sure how to go about doing this. Any help would be greatly appreciated!
 

1. What is the "Birthday problem variation"?

The Birthday problem variation, also known as the birthday paradox, is a mathematical problem that explores the probability of shared birthdays in a group of people. It is a variation of the classic birthday problem where the question asks how many people are needed in a group to have at least one shared birthday with a specific person.

2. How is the probability of shared birthdays calculated in this variation?

The probability of shared birthdays in this variation is calculated using the formula P(n) = 1 - (365!/((365-n)!*365^n)), where n represents the number of people in the group. This formula takes into account the number of possible combinations of birthdays and subtracts it from 1 to find the probability of at least one shared birthday.

3. What is the significance of the "Birthday problem variation" in real-life scenarios?

The birthday problem variation has real-life applications in fields such as cryptography, statistics, and insurance. In cryptography, it is used to analyze the probability of two people having the same password. In statistics, it is used to understand the likelihood of shared birthdays in a sample group. In insurance, it is used to calculate the risk of shared birthdates in a group of policyholders.

4. Can the "Birthday problem variation" be generalized to other events?

Yes, the birthday problem variation can be generalized to other events that involve a finite number of outcomes and a random selection process. For example, it can be applied to the probability of shared anniversaries in a group, or shared birth years in a group of people.

5. How does the size of the group affect the probability of shared birthdays in this variation?

The probability of shared birthdays increases as the size of the group increases. For example, with a group of 20 people, the probability of at least one shared birthday is approximately 41%, whereas with a group of 50 people, the probability increases to 97%. This is due to the larger number of possible combinations of birthdays in a larger group.

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