In the oil-wildcatting problem (book chapter 7), suppose that the company could collect information from a drilling core sample and analyze it to determine whether a dome structure exists at Site 1. A positive result would indicate the presence of a dome, and a negative result would indicate the absence of a dome. The test is not perfect, however. The test is highly accurate for detecting a dome; if there is a dome, then the test shows a positive result 99% of the time. On the other hand, if there is no dome, the probability of a negative result is only 0.85. Thus, P(+ | Dome) = 0.99 and P(- | No Dome) = 0.85. Use these probabilities, the information given in the example, and Bayes' theorem to find the posterior probabilities P(Dome | +) and P(Dome | -). If the test gives a positive result, which site should be selected? Calculate expected values to support your conclusion! If the test result is negative, which site should be chosen? Again, calculate expected values
In the oil-wildcatting problem (book chapter 7), suppose that the company could collect information from a drilling core sample and analyze it to determine whether a dome structure exists at Site 1. A positive result would indicate the presence of a dome, and a negative result would indicate the absence of a dome. The test is not perfect, however. The test is highly accurate for detecting a dome; if there is a dome, then the test shows a positive result 99% of the time. On the other hand, if there is no dome, the probability of a negative result is only 0.85. Thus, P(+ | Dome) = 0.99 and P(- | No Dome) = 0.85. Use these probabilities, the information given in the example, and Bayes' theorem to find the posterior probabilities P(Dome | +) and P(Dome | -). If the test gives a positive result, which site should be selected? Calculate expected values to support your conclusion! If the test result is negative, which site should be chosen? Again, calculate expected values
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