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16                     Fundamentals of Probability and Statistics for Engineers

           and, in the case of n events,

                     !
                 n        n         n  n           n   n   n
                [        X         XX             X X X
             P     A j  ˆ   P…A j †      P…A i A j †‡        P…A i A j A k †
                jˆ1      jˆ1       iˆ1 jˆ2         iˆ1 jˆ2 kˆ3          …2:15†
                                       i<j            i<j<k
                               ‡ … 1† n 1 P…A 1 A 2 ...A n †;

           where A j , j ˆ  1, 2, .. . , n, are arbitrary events.

             Example 2.5. Let us go back to Example 2.4 and assume that probabilities
                                                             [
                                                                         [
           P(A),  P(B), and P(C) are known. We wish to compute P(A   B) and P(A   C).
                           [
             Probability P(A C), the probability of having either 50 or fewer cars turn-
           ing left  or  between  80 to  100 cars turning left,  is simply P(A) ‡  P(C). This
           follows  from  Axiom  3,  since  A   and  C  are  mutually  exclusive.  However,
           P(A   B), the probability of having 60 or fewer cars turning left, is found from
              [
                             P…A [ B†ˆ P…A†‡ P…B†  P…AB†

           The information given above is thus not sufficient to determ ine this probability
           and  we need  the additional information, P(AB), which  is the probability  of
           having between 40 and 50 cars turning left.
             With the statement of three axioms of probability, we have completed the
           mathematical description of a random experiment. It consists of three funda-
           mental constituents: a  sample space S, a  collection  of events A, B, ... , and  the
           probability  function  P.  These  three  quantities  constitute  a  probability space
           associated with a random experiment.



           2.2.2  ASSIGNMENT  OF  PROBABILITY

           The axioms of probability define the properties of a probability measure, which are
           consistent with our intuitive notions. However, they do not guide us in assigning
           probabilities to various events. For problems in applied sciences, a natural way to
           assign the probability of an event is through the observation of relative frequency.
           Assuming that a random experiment is performed a large number of times, say n,
           then for any event A let n A  be the number of occurrences of A in the n trials and
           define the ratio  n A /n as the relative frequency of A. Under  stable or  statistical
           regularity conditions, it is expected that this ratio will tend to a unique limit as n
           becomes large. This limiting value of the relative frequency clearly possesses the
           properties required of the probability measure and is a natural candidate for
           the probability of A. This interpretation  is used, for  example, in  saying that  the








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