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Published byBryson Nabb Modified over 4 years ago

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Bayes Theorem

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Motivating Example: Drug Tests A drug test gives a false positive 2% of the time (that is, 2% of those who test positive actually are not drug users) And the same test gives a false negative 1% of the time (that is, 1% of those who test negative actually are drug users) If Joe tests positive, what are the odds Joe is a drug user? Insufficient information! Suppose we know that 1% of the population uses the drug?

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Setting Up the Drug Test Problem Let T be the set of people who test positive Let D be the set of drug users These are events, and Pr(T|D) is the probability that a drug user tests positive Pr(T|D) =.99 because the false negative rate is 1%, that is, 99% of drug users test positive, 1% test negative We want to know: What is Pr(D|T)? This is a very different question: What is the probability you are a drug user, given that you test positive?

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Bayes Theorem Theorem: If Pr(A) and Pr(B) are both nonzero, Proof. We know that by the definition of conditional probability: and similarly for Pr(B|A). Then divide the left and right sides of (*) by Pr(B|A)Pr(B).

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Bayes, v. 2 This enables us to calculate Pr(A|B) using only the absolute probability Pr(A) and the conditional probabilities Pr(B|A) and Pr(B|¬A). Proof. We know that Now multiply by Pr(B|A) and rewrite Pr(B) using the law of total probability.

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Drug Test again Suppose that a drug test has –2% false positives (that is, 2% of the people who test positive are not drug users ) –1% false negatives (1% of those who test negative are drug users). Suppose 1% of the population uses drugs. If you test positive, what are the odds you are actually a drug user?

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Drug test, contd Let D = Uses drugs Let T = Tests positive What is Pr(D|T)?

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If you fail the drug test, there is only one chance in three you are actually a drug user! How can this be? Think about it. –Out of 1000 people there are 10 drug users and 990 non-users. –Of those 990, 2% or almost 20 test positive. –Almost all of the 10 users also test positive. –So there are 2 non-users for every user, among those who test positive!

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FINIS

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Bayes’ Theorem -- Partitions Given two events, R and S, if P(R S) =1 P(R S) =0 then we say that R and S partition the sample space. More than 2 events.

Bayes’ Theorem -- Partitions Given two events, R and S, if P(R S) =1 P(R S) =0 then we say that R and S partition the sample space. More than 2 events.

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