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- https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/BFE_1201_Statistical_Methods_for_Finance_(Kuter)/03%3A_Probability_Topics/3.06%3A_Conditional_Probability_and_Bayes'_RuleIn computing a conditional probability we assume that we know the outcome of the experiment is in event \(B\) and then, given that additional information, we calculate the probability that the outcome...In computing a conditional probability we assume that we know the outcome of the experiment is in event \(B\) and then, given that additional information, we calculate the probability that the outcome is also in event \(A\). If we let \(C\) denote the event that the card is a club and \(K\) the event that it is a King, then we are looking to compute $$P(C\ |\ K) = \frac{P(C\cap K)}{P(K)}.\label{condproba}$$ To compute these probabilities, we count the number of outcomes in the following events:
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/03%3A_Probability_Topics/3.06%3A_Conditional_Probability_and_Bayes'_RuleThis page discusses conditional probability, detailing its definition and mathematical formulation, along with relevant examples like card drawing. It covers the Multiplication Law and Law of Total Pr...This page discusses conditional probability, detailing its definition and mathematical formulation, along with relevant examples like card drawing. It covers the Multiplication Law and Law of Total Probability for calculating probabilities across multiple events, and introduces Bayes' Rule for reverse conditional probabilities.
- https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/DSCI_500B_Essential_Probability_Theory_for_Data_Science_(Kuter)/02%3A_Conditional_Probability/2.01%3A_Conditional_Probability_and_Bayes'_RuleIn computing a conditional probability we assume that we know the outcome of the experiment is in event \(B\) and then, given that additional information, we calculate the probability that the outcome...In computing a conditional probability we assume that we know the outcome of the experiment is in event \(B\) and then, given that additional information, we calculate the probability that the outcome is also in event \(A\). If we let \(C\) denote the event that the card is a club and \(K\) the event that it is a King, then we are looking to compute $$P(C\ |\ K) = \frac{P(C\cap K)}{P(K)}.\label{condproba}$$ To compute these probabilities, we count the number of outcomes in the following events:
