10% of your emails are spam emails. Your spam filter catches spam 90% of the time. Your spam filter misidentifies non-spam as spam 3% of the time. How might you use probability to understand what percent of the emails sitting in your spam folder are genuinely spam emails? (Hint: Bayes’ Law might be useful; also think about false positives).
10% of your emails are spam emails. Your spam filter catches spam 90% of the time. Your spam filter misidentifies non-spam as spam 3% of the time. How might you use probability to understand what percent of the emails sitting in your spam folder are genuinely spam emails? (Hint: Bayes’ Law might be useful; also think about false positives).
Chapter8: Sequences, Series,and Probability
Section8.7: Probability
Problem 50E: Flexible Work Hours In a recent survey, people were asked whether they would prefer to work flexible...
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10% of your emails are spam emails. Your spam filter catches spam 90% of the time. Your spam filter misidentifies non-spam as spam 3% of the time. How might you use probability to understand what percent of the emails sitting in your spam folder are genuinely spam emails? (Hint: Bayes’ Law might be useful; also think about false positives).
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