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Unit 12: Probability and Expected Value




          It should be noted here that the sample space is certain  to occur since the  outcome of the  Notes
          experiment must always be one of its elements.

          Definition of Probability (Modern Approach)

          Let S be a sample space of an experiment and A be any event of this sample space. The probability
          of A, denoted by P(A), is defined as a real value set function which associates a real  value
          corresponding to a subset A of the sample space  S. In order that  P(A) denotes a probability
          function, the following rules, popularly known as axioms or postulates of probability, must be
          satisfied.
          Axiom I :  For any event A in sample space S, we have 0   P(A)   1.
          Axiom II : P(S) = 1.

          Axiom III : If A , A , ...... A  are k mutually exclusive events (i.e.,  A   A  , where  denotes
                       1  2     k                               i   j
                                                                i j
                    a null set) of the sample space S, then
                                                        k
                                   P A   A  ......     A k  P A i
                                      1
                                          2
                                                       i 1
          The first axiom implies that the probability of an event is a non-negative number less than or
          equal to unity. The second axiom implies that the probability of an event that is certain to occur
          must be equal to unity. Axiom III gives a basic rule of addition of probabilities when events are
          mutually exclusive.
          The above axioms provide a set of basic rules that can be used to find the probability of any
          event of a sample space.
          Probability of an Event


          Let there be a sample space consisting of n elements, i.e., S = {e , e , ...... e }. Since the elementary
                                                            1  2    n
                                                                               n
          events e , e , ...... e  are mutually exclusive, we have, according to axiom III,  P S  P e .
                 1  2   n                                                           i
                                                                              i  1
          Similarly, if A = {e , e , ...... e } is any subset of S consisting of m elements, where m   n, then
                         1  2    m
                   m
           P A       P e . Thus, the probability of a sample space or an event is equal to the sum of
                         i
                  i  1
          probabilities of its elementary events.
          It is obvious from the above that the probability of an event can be determined if the probabilities
          of elementary events, belonging to it, are known.

          The Assignment of Probabilities to various Elementary Events

          The assignment of probabilities to various elementary events of a sample space can be done in
          any one of the following three ways:
          1.   Using  Classical  Definition: We  know  that  various  elementary  events  of  a  random
               experiment, under the classical definition, are equally likely and, therefore, can be assigned
               equal probabilities.  Thus, if  there are  n elementary  events in  the sample  space of  an







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