The Monte Carlo Method

A recent post on Twitter contained a probability challenge:


I’ve fallen in love with the Monte Carlo method of using computers to simulate repeated random experiments. It’s a great way to get to the heart of what’s happening in a probability problem. It’s like being able to flip a coin or roll a die a million times using a computer. I went to work modeling the situation in Python:

from random import random

N_TRIALS = 1000000

trialList = [] #empty list for all the trials
for i in range(N_TRIALS):
    trial = [] # single Trial
    randA = random() #generate a couple of random numbers
    randB = random()
    if randA <= 0.2: #if A is successful:
        trial.append(1) #add it to the trial
    if randB <= 0.3: #same with B
    #so that's going to be like [1,0] or [1,1] for example
    trialList.append(trial) #add the trial to the trialList

So now we’ll have a million random trials. We’ll check for the ones in which A succeeded but B didn’t and so on. Now all we have to do is check for each possibility and we should get close to the real probability:

As = 0 #number of times A occurs
Bs = 0 #number of times B occurs
Cs = 0 #at least 1 of A or B
Ds = 0 #exactly 1 of A or B
A_given_D = 0
B_given_D = 0

#iterate over trialList and sum up the scores
for trial in trialList:
    if trial[0]: #[1,0] or [1,1]
        As += 1
    if trial[1]: #[0,1] or [1,1]
        Bs += 1
    if 1 in trial: #[0,1],[1,0] or [1,1]
        Cs += 1
    if sum(trial) == 1: #[0,1] or [1,0]
        Ds += 1
        if trial[0]: #[1,0] 
            A_given_D += 1
        if trial[1]: #[0,1]
            B_given_D += 1


#print the results

print("P(Exactly 1):",P_D)

The outcome is given below. The Center of Math posted the answers and mine came very close!

P(A): 0.1994
P(B): 0.2996
P(C): 0.4393
P(Exactly 1): 0.3794
P(A|D): 0.3679
P(B|D): 0.6320