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Probabilities of Events, Conditional Probabilities, and Bayes' Rule
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Probabilities of Events, Conditional Probabilities, and Bayes' Rule
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Pertemuan 6 -
7 PROBABILITAS Hdi Nasbey, M.Si Jurusan Fisika Fakultas Matematika dan Ilmu Pemgetahuan Alam
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Example 1 In
a certain population, 10% of the people can be classified as being high risk for a heart attack. Three people are randomly selected from this population. What is the probability that exactly one of the three are high risk? Define H: high risk N: not high risk P(exactly one high risk) = P(HNN) + P(NHN) + P(NNH) = P(H)P(N)P(N) + P(N)P(H)P(N) + P(N)P(N)P(H) = (.1)(.9)(.9) + (.9)(.1)(.9) + (.9)(.9)(.1)= 3(.1)(.9) 2 = .243 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
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Example 2 Suppose
we have additional information in the previous example. We know that only 49% of the population are female. Also, of the female patients, 8% are high risk. A single person is selected at random. What is the probability that it is a high risk female? Define H: high risk F: female From the example, P(F) = .49 and P(H|F) = .08. Use the Multiplicative Rule: P(high risk female) = P(H F) = P(F)P(H|F) =.49(.08) = .0392 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
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The Law of
Total Probability A P(A) = P(A S 1 ) + P(A S 2 ) + … + P(A S k ) = P(S 1 )P(A|S 1 ) + P(S 2 )P(A|S 2 ) + … + P(S k )P(A|S k ) 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | A S k A S 1 S 2…. S 1 S k
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Example We know:
P(F) = P(M) = P(H|F) = P(H|M) = From a previous example, we know that 49% of the population are female. Of the female patients, 8% are high risk for heart attack, while 12% of the male patients are high risk. A single person is selected at random and found to be high risk. What is the probability that it is a male? Define H: high risk F: female M: male .12 .08 .51 .49 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
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