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Home Math Background Probability Conditional Probability |
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| See also: Bayesian rule, independent events | ||
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Conditional Probability
A conditional probability is defined as the probability of an event, given that another event has occurred. This means that the probability for event A is effected by event B. Formally, a conditional probability is depicted as P(A | B) (read: the probability of the event A under the condition that event B occurred). Example:We toss a die and define the events A {even number} and B {number is less than or equal to 3}. What is the probability of A if somebody gives us a hint that B has occurred? When B is true, we have the possible sample points 1, 2 and 3. So after given this information, the probability that the number is even is now 1/3. Without this prior information the probability would have been 1/2.
P(A | B) = P(A This is true under the condition that P(B) is not equal to zero. The
equation adjusts the probability of A Intersection of eventsThe probability of an intersection of events is calculated by the multiplicative rule, which makes use of conditional probabilities. We simply re-arrange the equation for the conditional probability P(B| A) = P(A
B) / P(A) and obtain:
P(A Example:We have 10 marbles; 4 red and 6 blue, and take two of them randomly. We define the events A {the 1st marble is red} and B {the 2nd marble is red }. What is the probability that both marbles are red P(A
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Last Update: 2010-12-17