Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Chapter 4 The Mean and Variance 4.1 Expectations of random variables Although all the probability information of a random variable is contained in its cdf (or pmf for discrete random variables and pdf for continuous random variables), it is often useful to consider various numerical characteristics of that random variable. One such number is the expectation of a random variable; it is a sort of “weighted average” of the values that X can take. Here is a more precise definition. Mathematical expectation Definition 4.1 If X~P{X=xk}=pk, k=1,2,…n, define n E ( X ) x k pk k 1 the mathematical expectation of r.v. X or mean of X. Example1 Find EX if X is the outcome of a toss of a fair die. Since P(X =1)= ... = P(X =6)=1/6, we have n 1 1 1 1 1 1 E ( X ) xk pk 1 2 3 4 5 6 3.5 6 6 6 6 6 6 k 1 Note: E(X )is not necessarily a possible outcome of the random experiment as in the previous example. Mathematical expectation of several important distributions 1. 0-1 distribution X P 1 0 p 1 p E(X)=p 2. Binomial distribution X~B(n, p) P{ X k } C p (1 p) k n k n k k 0.1,...n n n! E( X ) k p k (1 p)n k k 1 k ! ( n k )! n n! p k (1 p) n k k 1 ( k 1)! ( n k )! n ( n 1)! np p k 1 (1 p ) n1 ( k 1) k 1 ( k 1)! ( n k )! n 1 令l k 1 np C nl 1 p l (1 p) n1 l l 0 np 3.Poisson distribution X ~ P{ X k } k k 0 k! E( X ) k k k! X ~ P e , k 0, 1, 2, ... e e k 1 k 1 (k 1)! ; Definition 4.1 Suppose that X~f(x), -<x<, then define E( X ) xf ( x )dx. the mathematical expectation of r.v. X Example :P74- Example 4.3 Mathematical expectation of several important distributions 1. Uniform distribution X~U(a, b) 1 , a x b, X ~ f ( x) b a 0, el se, E( X ) b a x ab dx ; ba 2 2.Exponential distribution X~Exp() 1 x e f ( x ) 0 1 X ~ Exp( ) x0 x0 x E ( X ) x e dx xde 0 xe x 0 0 x e dx 0 x 3. Normal distribution X~ N(, 2) 1 e 2 X ~ f (x) E( X ) 令t ( x ) 2 22 , x x e 2 x ( x )2 2 2 dx t 2 e t2 2 dt ; Mathematical expectation of the functions of r.v.s EX 1 Suppose that the distribution law of X X -1 Pk 1 3 0 1 1 1 3 3 Try to determine the mathematical expectation of Y=X2 Y Pk 1 2 3 0 1 3 2 1 2 E (Y ) 1 0 3 3 3 P-74 Theorem 4.1 let X~P{X=xk}=pk, k=1,2,…,then the mathematical expectation of Y=g(X) is given by the following equation and denoted by E(g(X)) E (Y ) E[ g ( X )] g ( x k ) pk . k 1 P-74 Theorem 4.1 If X~f(x), -<x<, the mathematical expectation of Y=g(X) is specified as E (Y ) E[ g( X )] g( x ) f ( x )dx. Suppose that X follows N(0,1) distribution, try to determine E(X2), E(X3) and E(X4) f ( x) E( X ) 2 1 e 2 1 e 2 2 x e 2 x2 2 x2 2 x2 2 dx 1 dx x de 2 x2 2 E( X ) 3 3 E( X ) 4 3 4 3 x2 2 x de 2 dx 0 x2 2 x e 2 x e 2 x2 2 dx 3 2 x e 2 x2 2 dx Properties of mathematical expectation 1. E(c)=c, where c is a constant; 2。E(cX)=cE(X), c is a constant. Proof. Let X~f(x), then E (cX ) cxf ( x )dx c xf ( x )dx cE ( X ) Example 2. Some disease will occur with probability 1%, investigate 1000 people now, it is necessary to check the blood. The method is clarified these people into ten group with each group 100 and check the mixed blood sample. If the result is negative, it is not need to do any test any more, if it is positive, then ,it is necessary to test each blood sample respectively, try to determine the average times needed for the test. Let Xj j 1,...10 is the number to be taken of jth group, and X the number to be taken in 1000 people, then Xj 1 101 Pj (99%)100 1 (99%)100 EX j 0.99100 (101)(1 0.99100 ) 10 10 j 1 j 1 E ( X ) E ( X j ) E ( X j ) 10[0.99100 (101)(1 0.99100 )] 1 1000 [1 0.99100 ] 100 644 Homework: P88: 1(2)(3)(4)(5),10Find the mean