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Unformatted text preview: Chapter 7.6: Bayes Theorem We have seen how occurrences of event in the past can affect proba bilities of future events, and we learned how to calculate these proba bilities using the conditional probability formula. There are occasions where we may wish to do the opposite. We might know how an experiment has ended, and wish to use this information to find out something about the past. Bayes Theorem tells us that the conditional probability formula we have already seen can be used for this purpose. Our text contains a formula for Bayes Theorem. There is no need to memorize this formula; it is equivalent to the conditional probability formula from the previous section. We will begin be revisiting a problem from the last section. Example: Suppose that 30% of all students drive to school, 50% take the bus, and 20% walk. Of those who drive, 20% are usually late for their first class of the day. Of those who take the bus, 10% are usually lake for their first class of the day. Of those who walk, 15% are usually late for their first class of the day. Suppose a randomly selected student is regularly latefirst class of the day....
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This note was uploaded on 02/05/2011 for the course MATH 377 taught by Professor Stephenlang during the Spring '11 term at University of Victoria.
 Spring '11
 stephenlang
 Calculus, Conditional Probability, Probability

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