Query re the Birthday Problem

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In summary, the conversation discusses the use of "365" and "365.25" in the standard approach for calculating leap days, as well as the consideration of other leap day rules and their impact on the empirical probability. There is also a request for including relevant URLs and ancillary information, and an apology to site coordinators. The question of which model is suitable for an undergraduate readership and converges to the empirical probability is also raised. The conversation ends with a thank you to all contributors and a request for permission to use site usernames in the final text for proper recognition.
  • #1
Ben2
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Has anyone seen this alternative?
Has any solver replaced "365" by "365.25" in the standard approach? I'm editing a book, and don't want unpleasant surprises when it appears. Please include URRL's and ancillary information. Finally let me apologize to site coordinators if this is off the reservation. Thanks!
 
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  • #2
You could take the standard approach and extend the period to 400 years to cover all possible leap day rules.
 
  • #3
Ben2 said:
Summary:: Has anyone seen this alternative?

Has any solver replaced "365" by "365.25" in the standard approach? I'm editing a book, and don't want unpleasant surprises when it appears. Please include URRL's and ancillary information. Finally let me apologize to site coordinators if this is off the reservation. Thanks!
It doesn't make a lot of difference, for the obvious reason.
 
  • #4
I was unaware of other leap day rules as posted by fresh_42, but may reference that in the book. On PeroK's comment, using 365 and 366 give answers differing by .001 to 4 d.p.'s; but 365.25 gives me a smaller probability.
The question is, Which model is simultaneously suitable for an undergrad readership and converges (Law of Large Numbers) to the empirical probability?
Thanks for all comments and references provided.
 
  • #5
Ben2 said:
The question is, Which model is simultaneously suitable for an undergrad readership and converges (Law of Large Numbers) to the empirical probability?
Thanks for all comments and references provided.
The empirical probability would take account of the non-uniform pattern in births throughout the year.
 
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Ben2 said:
I was unaware of other leap day rules as posted by fresh_42
An interesting consequence of which is that the 13th of the month falls on a Friday a little more often than 1/7.
 
  • #7
Thanks for all comments to this point. Since I've been trained to cite contributors at every opportunity, please
indicate if you do not want your site username used in the final text. Otherwise only my publishers or this site's gatekeepers will prevent appropriate recognition of your assistance.
 

1. What is the Birthday Problem?

The Birthday Problem, also known as the Birthday Paradox, is a mathematical problem that asks how many people are needed in a group for there to be a 50% or higher chance that two people share the same birthday.

2. How is the probability of shared birthdays calculated?

The probability of shared birthdays is calculated using the formula P(n) = 1 - (365!/((365-n)!*365^n)), where n is the number of people in the group.

3. What is the significance of the Birthday Problem?

The Birthday Problem is significant because it demonstrates the counterintuitive nature of probability. Many people assume that a group needs to be very large for there to be a high chance of shared birthdays, but in reality, the number is much smaller than expected.

4. How does the Birthday Problem apply to real life situations?

The Birthday Problem has many applications in real life, such as in cryptography, where it is used to demonstrate the potential for collisions in hashing algorithms. It also has implications in fields such as genetics and epidemiology, where it can be used to calculate the likelihood of shared genetic traits or the spread of diseases.

5. Are there any variations of the Birthday Problem?

Yes, there are variations of the Birthday Problem that take into account different factors such as leap years, non-uniform distribution of birthdays, and multiple shared birthdays. These variations can affect the probability of shared birthdays and may require different calculations.

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