Dependent theorem and bayes theorem.

In summary: I am currently working on a very very similar question on my own. But, the problem I have is finding the P(Random 2 are good). Until you do that, you will be unable to get anywhere. I will just give one more hint: what is P(both good|1 bad, 19 good in carton)? Well, you are GIVEN that the carton has 19 good eggs in it, and one bad egg. You draw 2 eggs at random, and want the probability that both are good. Surely you must have seen or done such problems before, but if not: (i) what is the probability the first drawn egg is good? (ii) given that the first egg is good, we now are left
  • #1
iTee
6
0

Homework Statement



One box of eggs has 12 eggs. The probability of all eggs being good is 70%. The probability of one being bad is 20%. The probability of two being bad is 10%. Given that 2 randomly selected eggs are good, what is the probability that all the eggs will be good?

Homework Equations





The Attempt at a Solution



According to bayes theorem,

P(all good | random 2 are good) = ( P(random 2 are good | all good) * P(all good) ) / P(random two are good)


Now, P(random 2 are good | all good) = P(random 2 are good <and> all good) / P(all good)

=> " P(random 2 are good <and> all good) " are dependent events. Can't seem to figure out how to get around this.
 
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  • #2
iTee said:

Homework Statement



One box of eggs has 12 eggs. The probability of all eggs being good is 70%. The probability of one being bad is 20%. The probability of two being bad is 10%. Given that 2 randomly selected eggs are good, what is the probability that all the eggs will be good?

Homework Equations





The Attempt at a Solution



According to bayes theorem,

P(all good | random 2 are good) = ( P(random 2 are good | all good) * P(all good) ) / P(random two are good)


Now, P(random 2 are good | all good) = P(random 2 are good <and> all good) / P(all good)

=> " P(random 2 are good <and> all good) " are dependent events. Can't seem to figure out how to get around this.

For b = 0,1,2, what are P(random 2 are good|12-b good and b bad)? So, what is P(random 2 are good)?

RGV
 
  • #3
I am currently working on a very very similar question on my own. But, the problem I have is finding the P(Random 2 are good).

Needed Information
P(all are good) = 0.7
P(Random 2 are good | all are good) = 1 (since given all are good, any random ones will be guranteed to be good)
P(Random 2 are good) This is where I struggle, how to find this?
 
  • #4
lovemake1 said:
I am currently working on a very very similar question on my own. But, the problem I have is finding the P(Random 2 are good).

Needed Information
P(all are good) = 0.7
P(Random 2 are good | all are good) = 1 (since given all are good, any random ones will be guranteed to be good)
P(Random 2 are good) This is where I struggle, how to find this?

Read the question immediately above this one. Have you done the two other computations? Until you do that, you will be unable to get anywhere. I will just give one more hint: what is P(both good|1 bad, 19 good in carton)? Well, you are GIVEN that the carton has 19 good eggs in it, and one bad egg. You draw 2 eggs at random, and want the probability that both are good. Surely you must have seen or done such problems before, but if not: (i) what is the probability the first drawn egg is good? (ii) given that the first egg is good, we now are left with 19 eggs (18 good, one bad), so now what is the probability of drawing another good egg?

RGV
 

1. What is the Dependent Theorem?

The Dependent Theorem, also known as the Dependent Events Theorem, is a mathematical rule used to calculate the probability of two or more dependent events occurring together. It states that the probability of event A and event B occurring together is equal to the probability of event A multiplied by the probability of event B given that event A has already occurred. This theorem is often used in statistics and probability to calculate the likelihood of certain outcomes.

2. What is the Bayes Theorem?

The Bayes Theorem, also known as Bayes' Rule or Bayes' Law, is a mathematical formula used to calculate the probability of an event occurring based on prior knowledge or information. It is named after the mathematician Thomas Bayes and is commonly used in statistics, machine learning, and other fields to update beliefs or predictions based on new evidence or data.

3. What is the difference between the Dependent Theorem and Bayes Theorem?

The Dependent Theorem is used to calculate the probability of two or more dependent events occurring together, while Bayes Theorem is used to update the probability of an event based on new evidence or information. The Dependent Theorem is a specific case of Bayes Theorem where the events are dependent on each other.

4. How is Bayes Theorem applied in real-life situations?

Bayes Theorem is used in a wide range of real-life situations, such as medical diagnosis, spam filtering, and weather forecasting. For example, in medical diagnosis, a doctor can use Bayes Theorem to calculate the probability of a patient having a certain disease based on their symptoms and prior knowledge of the disease.

5. What are the limitations of using Bayes Theorem?

Bayes Theorem relies heavily on the accuracy and completeness of the prior information or data used. If the prior information is biased or incomplete, it can lead to inaccurate predictions. Additionally, Bayes Theorem assumes that each event is independent, which may not always be the case in real-life situations.

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