Monty Hall Problem: Probability Calculation for Switching Doors

In summary, the game show host will reveal Door 3 (M3), and the contestant has a 2/3 chance of winning the car if they switch to Door 2.
  • #1
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Homework Statement



Suppose you’re on a game show and you’re given the choice of three doors. Behind one is a car, behind the others are goats. You pick a door, say number 1, and the host, who knows what’s behind the doors, opens another door, say number 3 which has a goat. He says to you, “Do you want to pick door number 2?” Is it to your advantage to switch your choice of doors?”

Use the General Case of Bayes’ Rule to demonstrate mathematically the 2/3 probability of getting the car by switching. Assume for sure that the contestant picked Door 1, that the game show host reveals Door 3 (M3), and you are going to let the car vary between the three doors as Ci where i = {1, 2, 3}. You can now calculate P(C2|M3), that is, the probability of winning the car given that the host reveals Door 3 and you switch to Door 2.

Homework Equations


General Case of Bayes Rule
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The Attempt at a Solution


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I started by changing the variables to match the problem. But I am not sure where to go from here.

I know that P(M3|C2) means "Probability of Door 3, given Door 2". So for those two I figure that it breaks down to a 1/2 probability. But I feel kind of stuck on which values are equal to what.
 

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  • #2
First you need to correct your formula under section 3. You have a sum over ##i##, but ##i## does not appear in the summand. Also, I suggest using ##j## instead, as this platform always auto-corrects ##i## to upper case.

##P(M_3|C_j)## is the probability that Monty Hall (the host) will open door 3, given that the car is behind door ##j##, and that you have picked door 1. Your original choice of door 1 is taken as a constant rather than a random event, in order to avoid double-barrelled conditionalities. We can achieve that by deferring the numbering of doors until after your original choice is made, and then numbering them so that yours is number 1.

You need to bear in mind that Monty will neither open a door you have picked, nor a door with a car behind it. Given that, what is ##P(M_3|C_j)## for each of ##j=1,2,3##, and what is each ##C_j##?
 

FAQ: Monty Hall Problem: Probability Calculation for Switching Doors

What is Bayes' Rule?

Bayes' Rule, also known as Bayes' Theorem or Bayes' Law, is a mathematical formula that describes the relationship between conditional probabilities. It is named after the 18th century mathematician Thomas Bayes.

How does Bayes' Rule apply to the "Car and Goats" problem?

In the "Car and Goats" problem, Bayes' Rule is used to calculate the probability of winning a car in a game show where there are three doors - one with a car behind it and the other two with goats. After initially choosing a door, the host reveals one of the remaining doors to have a goat behind it, then gives the contestant the option to switch their choice. Bayes' Rule helps to determine whether it is beneficial to switch or not.

What are the key components of Bayes' Rule?

Bayes' Rule consists of three key components: the prior probability, the likelihood, and the posterior probability. The prior probability is the initial belief or probability of an event occurring. The likelihood is the probability of observing certain evidence given a particular hypothesis. The posterior probability is the updated probability of the event occurring after new evidence is taken into account.

How is Bayes' Rule calculated?

Bayes' Rule is calculated by multiplying the prior probability by the likelihood, and then dividing the result by the sum of all possible outcomes. In the "Car and Goats" problem, the prior probability of choosing the car is 1/3, the likelihood of the host showing a goat behind one of the remaining doors is 1/2, and the sum of all possible outcomes is 1. Therefore, the probability of winning the car by switching is (1/3 * 1/2) / 1, which equals 1/6 or approximately 16.7%.

What are some real-world applications of Bayes' Rule?

Bayes' Rule has many real-world applications, including in medicine, finance, and artificial intelligence. It is used in medical diagnosis to calculate the probability of a patient having a certain disease based on their symptoms and test results. In finance, it is used to update predictions and make investment decisions based on new information. In artificial intelligence, Bayes' Rule is used in Bayesian networks to model and make decisions based on uncertain information.

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