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                            2                     Properties of Probability Distributions

                              (P3) If A 1 , A 2 ,..., are disjoint subsets of  , then

                                                            ∞         ∞

                                                         P     A n  =   P(A n ).
                                                            n=1      n=1
                              Note that (P3) implies (P2); however, (P3), which is concerned with an infinite sequence
                            of events, is of a different nature than (P2) and it is useful to consider them separately.
                            There are a number of straightforward consequences of (P1)–(P3). For instance, P(∅) = 0,
                                                               c
                               c
                            if A denotes the complement of A, then P(A ) = 1 − P(A), and, for A 1 , A 2 not necessarily
                            disjoint,
                                              P(A 1 ∪ A 2 ) = P(A 1 ) + P(A 2 ) − P(A 1 ∩ A 2 ).

                            Example 1.1 (Sampling from a finite population). Suppose that   is a finite set and that,
                            for each ω ∈  ,

                                                           P({ω}) = c
                            for some constant c. Clearly, c = 1/| | where | | denotes the cardinality of  .
                              Let A denote a subset of  . Then
                                                                 |A|
                                                           P(A) =   .
                                                                 | |
                            Thus, the problem of determining P(A)is essentially the problem of counting the number
                            of elements in A and  .

                            Example 1.2 (Bernoulli trials). Let
                                                 n
                                         ={x ∈ R : x = (x 1 ,..., x n ), x j = 0or 1,  j = 1,..., n}
                            so that an element of   is a vector of ones and zeros. For ω = (x 1 ,..., x n ) ∈  , take
                                                             n

                                                                x j
                                                     P(ω) =    θ (1 − θ) 1−x j
                                                            j=1
                            where 0 <θ < 1isagiven constant.

                            Example 1.3 (Uniform distribution). Suppose that   = (0, 1) and suppose that the prob-
                            ability of any interval in   is the length of the interval. More generally, we may take the
                            probability of a subset A of   to be

                                                          P(A) =   dx.
                                                                 A
                              Ideally, P is defined on the set of all subsets of  . Unfortunately, it is not generally
                            possible to do so and still have properties (P1)–(P3) be satisfied. Instead P is defined only
                            on a set F of subsets of  ;if A ⊂   is not in F, then P(A)is not defined. The sets in F
                            are said to be measurable. The triple ( , F, P) is called a probability space; for example,
                            we might refer to a random variable X defined on some probability space.
                              Clearly for such an approach to probability theory to be useful for applications, the set
                            F must contain all subsets of   of practical interest. For instance, when   is a countable
                            set, F may be taken to be the set of all subsets of  . When   may be taken to be a
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