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Unformatted text preview: CS 702 Discrete Mathematics and Probability Theory Spring 2009 Alistair Sinclair, David Tse Note 11 Conditional Probability A pharmaceutical company is marketing a new test for a certain medical condition. According to clinical trials, the test has the following properties: 1. When applied to an affected person, the test comes up positive in 90% of cases, and negative in 10% (these are called false negatives). 2. When applied to a healthy person, the test comes up negative in 80% of cases, and positive in 20% (these are called false positives). Suppose that the incidence of the condition in the US population is 5%. When a random person is tested and the test comes up positive, what is the probability that the person actually has the condition? (Note that this is presumably not the same as the simple probability that a random person has the condition, which is just 1 20 .) This is an example of a conditional probability : we are interested in the probability that a person has the condition (event A ) given that he/she tests positive (event B ). Lets write this as Pr [ A  B ] . How should we define Pr [ A  B ] ? Well, since event B is guaranteed to happen, we should look not at the whole sample space , but at the smaller sample space consisting only of the sample points in B . What should the conditional probabilities of these sample points be? If they all simply inherit their probabilities from , then the sum of these probabilities will be B Pr [ ] = Pr [ B ] , which in general is less than 1. So we should normalize the probability of each sample point by 1 Pr [ B ] . I.e., for each sample point B , the new probability becomes Pr [  B ] = Pr [ ] Pr [ B ] . Now it is clear how to define Pr [ A  B ] : namely, we just sum up these normalized probabilities over all sample points that lie in both A and B : Pr [ A  B ] : = A B Pr [  B ] = A B Pr [ ] Pr [ B ] = Pr [ A B ] Pr [ B ] . Definition 11.1 (conditional probability) : For events A , B in the same probability space, such that Pr [ B ] > 0, the conditional probability of A given B is Pr [ A  B ] : = Pr [ A B ] Pr [ B ] . Lets go back to our medical testing example. The sample space here consists of all people in the US denote their number by N (so N 250 million). The population consists of four disjoint subsets: CS 702, Spring 2009, Note 11 1 TP : the true positives (90% of N 20 = 9 N 200 of them); FP : the false positives (20% of 19 N 20 = 19 N 100 of them); TN : the true negatives (80% of 19 N 20 = 76 N 100 of them); FN : the false negatives (10% of N 20 = N 200 of them). Now let A be the event that a person chosen at random is affected, and B the event that he/she tests positive....
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This note was uploaded on 09/06/2009 for the course CS 70 taught by Professor Papadimitrou during the Spring '08 term at University of California, Berkeley.
 Spring '08
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