Is Bayes Theorem Correct for Calculating Probability of a Bomb?

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In summary, The slide from the lecture discusses a test for the presence of a bomb, where the probability of the bomb (P(B)) is calculated by dividing the total number of times the bomb was present by the total number of times the bomb was absent. However, it is suggested that the correct calculation would be dividing by the total number of outcomes, including positive and negative results. Similarly, the probability of a positive test (P(T)) is calculated by dividing the total number of positive tests by the total number of outcomes, but it is argued that the correct calculation would be dividing by the total number of positive tests only. It is also noted that the calculations in the slide may be incorrect and should be 1/1,000,
  • #1
Math Is Hard
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This is a slide from a lecture I had:
http://www.geocities.com/thesquarerootoftwo/bayes.JPG
What we're looking at is a test for the presence of a bomb, like something airport screeners might use.
When the probability of the bomb, P(B) is figured, it looks like the calculation is done by taking the total number of times the bomb was present and dividing it by the total number of times the bomb was absent.

Is this correct? I was thinking you would take the total number of times the bomb was present and divide by (the total number of times absent + the total number of times present).

I have the same question about P(T) which I think is the probability of a positive test.

But I don't know jack about Bayes Theorem, so thanks for any help!:smile:
 
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  • #2
I am not sure what you're asking. But it seems like you are trying to divide by the total number of outcomes (all types of outcomes, postive/negative and bomb/nobomb.

The notation, P(B|T), means the probablity of getting B (bomb), given that T(test) is positive.

So this means the total outcome set would be all of those that tested positve. You are only considering the probability realtive to everything that tested positive.
 
  • #3
Hi Cyrus, I'm asking if the way P(B) and P(T) are calculated in the slide is correct.
 
  • #4
Edit:

I think P(B) should be 1/1,000,101

and P(T) should be 101/1,000,101

Because the difference between 1,000,101 and 1,000,100 is small the answer will be the same. But if this were small numbers, the difference would be big.

I see what you mean now. I think it is wrong.
 
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  • #5
Thank you, Cyrus!
 

1. What is Bayes' theorem?

Bayes' theorem is a mathematical formula that describes the probability of an event occurring based on prior knowledge or information. It is named after the 18th-century British mathematician Thomas Bayes.

2. How is Bayes' theorem used in science?

Bayes' theorem is used in science to update our beliefs about a hypothesis based on new evidence. It allows us to combine prior knowledge and new information to make more accurate predictions or decisions.

3. What is the difference between a Bayesian and a frequentist approach?

A Bayesian approach to statistics involves using probability to represent uncertainty and updating beliefs as new evidence is obtained. A frequentist approach, on the other hand, relies on collecting and analyzing data to make inferences about the population.

4. Can Bayes' theorem be applied to any problem?

Yes, Bayes' theorem can be applied to any problem that involves making predictions or decisions based on uncertain information. It has been successfully used in various fields such as medicine, finance, and artificial intelligence.

5. Is Bayes' theorem always correct?

Bayes' theorem is a mathematical formula and is thus always correct. However, the accuracy of its predictions may be affected by the quality of the prior information and the assumptions made in the model. It is important to carefully consider these factors when using Bayes' theorem in real-world applications.

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