Another Bayes' formula problem

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In summary, we are given three identical cards with different colors on each side. We are asked to find the probability that a randomly selected card with a red side on top will have a black side on the bottom. After considering the symmetry of the cards, we can conclude that the probability is 1/3.
  • #1
Proggy99
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Homework Statement


There are three identical cards that differ only in color. Both sides of one are black, both sides of the second are red, and one side of the third card is black and its other side is red. These cards are mixed up and one of them is selected at random. If the upper side of this card is red, what is the probability that its other side is black?

Homework Equations


A is upper red, Y is lower black
P(A) = 3/6
P(Y) = 3/6

I did 3/6 because I considered 3 sides are red and 3 are black, but this keeps nagging at me like I am missing something.

The Attempt at a Solution


P(Y|A) = P(A|Y)P(Y) / [P(A|Y)P(Y) + P(A|Yc)P(Yc)] =
1/3 * 3/6 / [1/3 * 3/6 + 1/3 * 3/6] = .5

I believe this is right, but it almost seems to be too easy to get to this without using Bayes' formula at all, but that might just be a coincidence due to the easy numbers being manipulated. Thanks for any help
 
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  • #2
I'm not so sure. You give P(A|Y) = 1/3, but: if the front is red, the back could (a priori) be either black or red. I'd say P(A|Y) = 1/2.

Actually I'm going to change notation to B and R 'cause I can't keep A and Y straight.
I learned Bayes' theorem as:

p(B|R) = p(R|B) p(B) / P(R)
where p(B|R) is probability that back is black given that front is red.

The total prob of finding black or red is the same, p(B) = p(R) = 1/2,
and p(R|B) = 1/2 by the reasoning given above.
So p(B|R) = 1/2

As you point out, we didn't need Bayes' theorem to get that. So you got the right answer, maybe the wrong way.
 
Last edited:
  • #3
and p(R|B) = 1/2 by the reasoning given above.
So p(B|R) = 1/2

This is wrong If you pick a card, and it has red on one side, 2/3s of the time it's going to be the card with two red sides, and 1/3 of the time it's going to be the card with the black side. So the probability of seeing black on the other side given that you've seen red on one side is going to be 1/3.

Considering the symmetry of A and Y, calculating P(Y|A) from P(A|Y) is a bad plan to start with since you know they're the same. Instead let X be the event you got the card with one color on each side, and R the event that you see red on the side you're looking at. Find

P(X|Y) = P(Y and X) / P(Y)

P(Y and X) = 1/6 by considering there are six sides you could see, and exactly one of them is the red side on the multicolored card. P(Y) = 1/2
 
  • #4
ah, that makes perfect sense. Thank you! I'll apply my earlier comment to myself: right answer but wrong method!
 
  • #5
ok, that helped a lot, thanks
 

What is Bayes' formula?

Bayes' formula, also known as Bayes' theorem or Bayes' rule, is a mathematical formula that describes the probability of an event based on prior knowledge of related conditions or events.

How is Bayes' formula used in science?

Bayes' formula is widely used in various fields of science, including statistics, machine learning, and artificial intelligence. It is used to update the probability of a hypothesis as new evidence or information becomes available.

What is the general form of Bayes' formula?

The general form of Bayes' formula is P(A|B) = P(B|A) * P(A) / P(B), where P(A|B) is the probability of event A occurring given that event B has occurred, P(B|A) is the probability of event B occurring given that event A has occurred, P(A) is the prior probability of event A, and P(B) is the prior probability of event B.

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

Bayes' formula has many real-world applications, including medical diagnosis, spam filtering, weather forecasting, and stock market analysis. It is also used in courtrooms to calculate the likelihood of a defendant's guilt based on evidence and witness testimony.

What are the limitations of Bayes' formula?

One limitation of Bayes' formula is that it assumes independence between events, which may not always hold true in real-world situations. Additionally, it relies on accurate prior probabilities, which may be difficult to determine in some cases. It also does not account for the possibility of new evidence changing the prior probabilities.

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