| Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. |

Table of Contents Math Background Introduction to Probability Conditional Probability |
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| See also: Bayesian rule, independent events | ||
Conditional ProbabilityA 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 Ç B) / P(B) This is true under the condition that P(B) is not equal to zero. The
equation adjusts the probability of A Ç
B from its original value in the whole sample space to the probability
in the reduced sample space B.
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 Ç B) / P(A) and obtain:P(A Ç B) = P(A) . P(B|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 Ç B)?
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