Calculating Probability of Coin Selection Using Bayes Rule

In summary: W5nIHRvIGZpbmQgUFMvRDMgPSBVc2VkOiBbUC1kMyBdIFsvUC1kMyddIC8gW1AtZDMgXSB8IFs8L1MtUykgLz4gW1AtZDMgXQ0KRm9yIFBQKDh8MzAwKSB8ID8gW2QgXQ0KIFByLkQzL1MgPSBbMSooMS8zKSQvWzAqLjIpIC8gWzEvM3MgKSAvIFswLjUgYi
  • #1
ifly2hi
5
0

Homework Statement


I am just wanting to get a good starting point... not an answer to the question. Question: A desk contains three drawers. Drawer 1 has two gold coins. Drawer 3 has one gold coin and one silver coin. Drawer 3 has two silver coins. I randomly choose a drawer and then randomly choose a coin. If a silver coin is selected, what is the probability that i choose drawer 3? I am suppose to use Bayes rule to find probability. Thank you for any help


Homework Equations


Bayes Rule that I used: P(S/D3)= [P(D3/S)P(D3)] / [P(D1/S)P(S) + P(D2/S)P(S)+P(D3/S)P(S)]

The Attempt at a Solution


I defined all I could: Drawer(D), Silver(S) and Gold(G).
P(D1)=P(D2)=P(D3)= 1/3 b/c he has a one in three chance of choosing drawer 3.
Pr(D1/S)=0(bc there are no silver coins in drawer 1)
Pr(D2/S)=0.5 (bc there is only 1 silver coin out of two coins in drawer 2)
Pr(D3/S)=1.0 (bc there are two coins in drawer 3 and both are silver)

P(S/D3)= [1*(1/3)] / [0+(.5*1/3) + (1*1/3)] =0.67% that it will come from drawer 3.

Am I close?
 
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  • #2
ifly2hi said:

Homework Statement


I am just wanting to get a good starting point... not an answer to the question. Question: A desk contains three drawers. Drawer 1 has two gold coins. Drawer 3 has one gold coin and one silver coin. Drawer 3 has two silver coins. I randomly choose a drawer and then randomly choose a coin. If a silver coin is selected, what is the probability that i choose drawer 3? I am suppose to use Bayes rule to find probability. Thank you for any help


Homework Equations


Bayes Rule that I used: P(S/D3)= [P(D3/S)P(D3)] / [P(D1/S)P(S) + P(D2/S)P(S)+P(D3/S)P(S)]

The Attempt at a Solution


I defined all I could: Drawer(D), Silver(S) and Gold(G).
P(D1)=P(D2)=P(D3)= 1/3 b/c he has a one in three chance of choosing drawer 3.
Pr(D1/S)=0(bc there are no silver coins in drawer 1)
Pr(D2/S)=0.5 (bc there is only 1 silver coin out of two coins in drawer 2)
Pr(D3/S)=1.0 (bc there are two coins in drawer 3 and both are silver)

P(S/D3)= [1*(1/3)] / [0+(.5*1/3) + (1*1/3)] =0.67% that it will come from drawer 3.

Am I close?

No, you are not. P(S|D3) is obtainable directly from the data (drawer 3 has one gold and one silver coin). You were not asked to find P(S|D3); you want to know P(D3|S). Also, 0.67% = 0.0067 ≈ 2/300. Maybe you meant 67%.

RGV
 

What is Bayes Rule and how is it used to calculate probability?

Bayes Rule is a mathematical formula used to calculate the probability of an event occurring based on prior knowledge. It is used in conjunction with conditional probabilities, which are the probabilities of an event occurring given that another event has already occurred.

What is the formula for calculating probability using Bayes Rule?

The formula for Bayes Rule 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 occurring, and P(B) is the prior probability of event B occurring.

How is Bayes Rule applied to coin selection?

Bayes Rule can be applied to coin selection by using it to calculate the probability of selecting a certain coin from a group of coins. This can be done by first identifying the prior probability of selecting each coin, then using conditional probabilities to adjust the probabilities based on any additional information or prior knowledge about the coins.

What is the difference between prior probability and conditional probability?

Prior probability is the probability of an event occurring without any additional information or prior knowledge. Conditional probability is the probability of an event occurring given that another event has already occurred. In the context of coin selection, prior probability would be the probability of selecting a certain coin without knowing any information about the coins, while conditional probability would be the probability of selecting a certain coin given some information about the coins.

What are some real-world applications of calculating probability using Bayes Rule?

Bayes Rule can be used in a variety of real-world applications, such as medical diagnosis, spam filtering, and predicting the outcome of sports games. It is also commonly used in machine learning and artificial intelligence algorithms.

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