Urn Conditional Probability Problem

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Discussion Overview

The discussion revolves around a conditional probability problem involving an urn containing red, green, and yellow balls. Participants are exploring how to calculate the probability that the third ball drawn is yellow given that the first ball drawn is green. The conversation includes attempts to apply Bayes' theorem and the use of probability tree diagrams.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant states the problem and expresses uncertainty about applying Bayes' theorem.
  • Another suggests using a probability tree diagram to work out the probabilities of different combinations of drawn balls.
  • A participant attempts to calculate the probabilities for various combinations of balls drawn, providing specific calculations for scenarios where the first ball is green and the third is yellow.
  • Subsequent posts reiterate the calculations and clarify that the computed probabilities represent the joint probability of the third ball being yellow and the first being green.
  • A later reply points out an interesting equivalence in the probabilities of drawing a yellow ball in the second position, suggesting that this could simplify the calculations.
  • Participants express a desire for confirmation on their calculations and understanding of the problem.

Areas of Agreement / Disagreement

There is no consensus on the correct application of Bayes' theorem or the final probability calculation. Participants are exploring different approaches and calculations without reaching a definitive conclusion.

Contextual Notes

Some participants express uncertainty about setting up probability trees and the application of Bayes' theorem, indicating potential gaps in their understanding of the problem's requirements.

Rosalie
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The problem is stated as follows:

You have an urn with 12 red balls, 20 green balls, and 13 yellow balls. Suppose 3 balls are drawn without replacement. What is the probability the third ball is yellow given the first ball is green.

I am pretty sure I use Bayes formula, but I am not certain how to apply it in this case. I am really just baffled! Can anyone help me?
 
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First step is to work out the probability of each combination of 3 balls. Might be easiest with the aid of a probability tree diagram.
 
Im not certain I understand how to set up a probability tree for a problem like this. I am going to try to work it through. Give me a moment, I'll let you double check my results.
 
Ok! I think I got it. This is what I concluded.
Total Balls=45
Green(G)=20
Red(R)=12
Yellow(Y)

P(G,R,Y)=(20/45) X (12/44) X (13/43)=(52/1419)
P(G,G,Y)=(20/45) X (19/44) X (13/43)=(247/4257)
P(G,Y,Y)=(20/45) X (13/44) X (12/43)=(52/1419)
(52/1419) + (247/4257) + (52/1419) = (13/99)

If this is incorrect please let me know! It is very important I get this question correct. Thank you for all your help.
 
Rosalie said:
P(G,R,Y)=(20/45) X (12/44) X (13/43)=(52/1419)
P(G,G,Y)=(20/45) X (19/44) X (13/43)=(247/4257)
P(G,Y,Y)=(20/45) X (13/44) X (12/43)=(52/1419)
(52/1419) + (247/4257) + (52/1419) = (13/99)

You're half-way there - what you've worked out is the probability that 3rd is yellow AND 1st is green. Next step uses Bayes formula.
 
Rosalie said:
Ok! I think I got it. This is what I concluded.
Total Balls=45
Green(G)=20
Red(R)=12
Yellow(Y)

P(G,R,Y)=(20/45) X (12/44) X (13/43)=(52/1419)
P(G,G,Y)=(20/45) X (19/44) X (13/43)=(247/4257)
P(G,Y,Y)=(20/45) X (13/44) X (12/43)=(52/1419)
(52/1419) + (247/4257) + (52/1419) = (13/99)

If this is incorrect please let me know! It is very important I get this question correct. Thank you for all your help.

I can't resist. Did you notice that the value for P(G,Y) is also 13/99?

This is no accident. As long as you have no information about the second ball, the third ball and the second ball have exactly the same probability distributions. An easy way to get this first step would be to observe this equivalence and then just calculate P(G,Y) = (20/45)*(13/44)

It all makes no difference to the next step, where you apply Bayes theorem to get the required conditional probability.

Cheers -- sylas
 

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