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Created Sep 15, 2013
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A Python script to estimate the probabilities of the birthday problem ( for subgroups of size 2, 3, 4, 5, and 6 that have the same birthday.
import random
maxBirthdays = 6
numDays = 365
numTrials = 100000
maxGroupSize = 1000
def BirthdayExperiment():
birthdays = [0] * numDays
numToDuplicates = {}
r = random.Random()
numPeople = 0
while True:
numPeople += 1
nextBirthday = r.randint(0,numDays-1)
birthdays[nextBirthday] += 1
numBirthdays = birthdays[nextBirthday]
if (numBirthdays > 1) and (not numBirthdays in numToDuplicates):
numToDuplicates[numBirthdays] = numPeople
if numBirthdays == maxBirthdays:
return numToDuplicates
cumDistFunctions = {}
for i in range(2,maxBirthdays+1):
cumDistFunctions[i] = [0] * (maxGroupSize+1)
for trial in range(numTrials):
numToDuplicates= BirthdayExperiment()
for i in range(2,maxBirthdays+1):
for j in range(numToDuplicates[i],maxGroupSize+1):
cumDistFunctions[i][j] += 1
line = "# People,2 analytic"
for i in range(2,maxBirthdays+1):
line = "%s,%d same" % (line, i)
print line
analytic = 1.0
for numPeople in range(1,maxGroupSize+1):
if numPeople <= numDays:
analytic *= float(numDays-numPeople+1)/numDays
line = "%d,%f" % (numPeople, 1-analytic)
for i in range(2,maxBirthdays+1):
line = "%s,%f" % (line, float(cumDistFunctions[i][numPeople])/numTrials)
print line
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anrieff commented Jul 29, 2016

We were discussing this problem on an internet forum and came across your solution. Just FYI, the probability can be computed exactly, using a recursive counting function that computes the exact number of suitable combinations, and dividing that by 365**numPeople. I can post code later. For lots of people (e.g., > 100) it becomes slow due to the sheer size of the numbers involved. We were solving it for 60 people, where it finishes in a second or two.

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anrieff commented Jul 29, 2016

Here's my code:

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