Probability - using Bayes Theorem

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  • #1
tapciv
5
0

Homework Statement


Question breaks down to this.

defect occurs 1/100 items.

.97 (97%) of the time when an item has a defect it is detected.
.005 of the time, an item is detected to have a defect when it actually does not have one.

What is the probability that an Actual defect occurs when one is detected?


Homework Equations


I can use Bayes theorem, once I know the variables but this is where I am having trouble with this question.

Determining what A1, A2 are?
B = A defect being found (I believe)
P(B|A1)= ?
P(B|A2)=?

The Attempt at a Solution



I believe I want to find P(A1|B) which will be the probability that a detection is actually a defect when found.

I know P(B|A1), P(B|A2) must = 1 which is where I can not seem to figure out in this case.

I think A1 = Defect being found correctly = .97
and
A2 = Defect being found incorrectly = ? (.005 but is that it? or how is this calculated given this is 99/100 times .005 are found incorrectly?).

Any help would be awesome!

Thanks
 
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  • #2
I find it most helpful in such problems to use a suggestive notation, such as:
AD = actually defective, AN = actually non-defective,
DD = detected as defective and DN = detected as non-defective.
You are given P{AD} = 1/100, so you can get P{AN} (how?). You are also given
P{DD|AD} = 0.97, so you can get P{DN|AD} (how?). Finally, you are given P{DD|AN} = 0.005, so you can get P{DN|AN} (how?).

Now you want to compute P{AD|DD}. You can use the standard formulas to get this, but I won't spoil your fun by showing you how.

RGV
 

1. What is Bayes Theorem?

Bayes Theorem is a mathematical formula that helps us calculate the probability of an event occurring, given some prior knowledge or information. It is often used in statistics and data analysis to update our beliefs about the likelihood of an event as we gather more evidence.

2. How is Bayes Theorem used in science?

Bayes Theorem is used in science to make predictions or decisions based on incomplete or uncertain information. It allows scientists to incorporate new data or evidence into their existing knowledge to update their beliefs and make more accurate predictions.

3. What is the difference between prior probability and posterior probability?

Prior probability refers to the initial belief or probability of an event occurring, before any new evidence is taken into account. Posterior probability, on the other hand, is the updated probability of an event occurring after taking new evidence into consideration.

4. Can Bayes Theorem be applied to real-world situations?

Yes, Bayes Theorem can be applied to a wide range of real-world situations, such as medical diagnosis, weather forecasting, and financial analysis. It is a powerful tool for making predictions and decisions based on limited information.

5. What are some limitations of Bayes Theorem?

One limitation of Bayes Theorem is that it assumes the prior and posterior probabilities are accurate and unbiased. In reality, these probabilities may be influenced by personal beliefs or biases. Additionally, Bayes Theorem may not be suitable for complex or dynamic situations, as it relies on fixed probabilities and does not account for changing circumstances.

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