Intro probability 3

1,425 views
1,330 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,425
On SlideShare
0
From Embeds
0
Number of Embeds
115
Actions
Shares
0
Downloads
23
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Intro probability 3

  1. 1. Probability Theory Expectation Phong VO vdphong@fit.hcmus.edu.vn September 11, 2010 – Typeset by FoilTEX –
  2. 2. Expectation of a Random Variable Definition 1. The expected value, or mean, or first moment, of X is defined to be x xf (x) if X is discrete E(X) = xdF (x) = xf (x)dx if X is continuous assuming that the sum (or integral) is well-defined. We use the following notation to denote the expected value of X: E(X) = EX = xdF (x) = µ = µX – Typeset by FoilTEX – 1
  3. 3. • Think of E(X) as the average value you would obtain if you computed n the numerical average n−1 i=1 Xi of a large number of IID draws X1, . . . , Xn. Example 1. Find E[X] where X is the outcome when we roll a fair dice. Example 2. Calculate E(X) when X is a Bernoulli random variable with parameter p. Example 3. Calculate E(X) when X is binomially distributed with parameters n and p. Example 4. Calculate the expectation of a geometric random variable having parameter p. Example 5. Calculate E(X) if X is a Poisson random variable with parameter λ. – Typeset by FoilTEX – 2
  4. 4. Example 6. Calculate the expectation of a random variable uniformly distributed over (α, β) Example 7. Let X be exponentially distributed with parameter λ. Calculate E(X). Example 8. Calculate E(X) when X is normally distributed with parameters µ and σ 2 – Typeset by FoilTEX – 3
  5. 5. Expectation of a Function of a Random Variable • If X is a discrete random variable with probability mass function p(x), then for any real-valued function g, E[g(X)] = g(x)p(x) x:p(x)>0 • If X is a continuous random variable with probability density function f (x), then for any real-valued function g, ∞ E[g(X)] = g(x)f (x)dx −∞ – Typeset by FoilTEX – 4
  6. 6. Example 9. Let (X, Y ) have a jointly uniform distribution on the unit square. Let Z = r(X, Y ) = X 2 + Y 2. Then, E(Z) = r(x, y)dF (x, y) (1) 1 1 = (x2 + y 2)dxdy (2) 0 0 1 1 = x2dx + y 2dy (3) 0 0 2 = (4) 3 – Typeset by FoilTEX – 5
  7. 7. Properties of Expectations Theorem 1. If X1, . . . , Xn are r.vs and a1, . . . , an are constants, then E aiXi = a + iE(Xi) i i Theorem 2. Let X1, . . . , Xn be independent r.vs. Then, n E Xi = aiE(Xi). i=1 i – Typeset by FoilTEX – 6
  8. 8. Example 10. Let X ∼ Binomial(n, p). What is the mean of X? We could try to appeal to the definition: n n E(X) = xdFX (x) = xfX (x) = x px(1 − p)n−x x x x=0 n but this is not an easy way to evaluate. Instead, note that X = i=1 Xi where Xi = 1 if the ith toss is heads and Xi = 0 otherwise. Then E(Xi) = (p × 1) + ((1 − p) × 0) = p and E(X) = E( i Xi) = i E(Xi) = np. – Typeset by FoilTEX – 7
  9. 9. Variance and Covariance Definition 2. Let X be a r.v with mean µ. The variance of X, denoted by σ 2 or σX or V (X) or V X, is defined by 2 σ 2 = E(X − µ)2 = (x − µ)2dF (x) assuming this expectation exists. The standard deviation is sd(X) = V (X) and is also denoted by σ and σX . – Typeset by FoilTEX – 8
  10. 10. Theorem 3. Assuming the variance is well defined, it has the following properties: 1. V (X) = E(X 2) − µ2. 2. If a and b are constants then V (aX + b) = a2V (X). 3. If X1, . . . , Xn are independent and a1, . . . , an are constants, then n n V aiXi = a2V (X + i). i i=1 i=1 – Typeset by FoilTEX – 9
  11. 11. • If X1, . . . , Xn are r.vs then we define the sample mean to be n 1 Xn = Xi n i=1 and the sample variance to be n 2 1 Sn = (Xi − Xn)2. n − 1 i=1 – Typeset by FoilTEX – 10
  12. 12. Theorem 4. Let X1, . . . , Xn be IID and let µ = E(Xi), σ 2 = V (Xi). Then σ2 E(Xn = µ, V (Xn) = and E(Sn) = σ 2. 2 n – Typeset by FoilTEX – 11
  13. 13. Definition 3. Let X and Y be r.vs. with means µX and µY and standard deviations σX and σY . Define the covrariance between X and Y by Cov(X, Y ) = E[(X − µX )(Y − µY )] and the correlation by Cov(X, Y ) ρ = ρX,Y = ρ(X, Y ) = σX σY – Typeset by FoilTEX – 12
  14. 14. Theorem 5. The covariance satisfies: Cov(X, Y ) = E(XY ) − E(X)E(Y ). The correlation satisfies: −1 ≤ ρ(X, Y ) ≤ 1. Theorem 6. V (X + Y ) = V (X) + V (Y ) + 2Cov(X, Y ) and V (X − Y ) = V (X) + V (Y ) − 2Cov(X, Y ). More generally, for r.vs X1, . . . , Xn, V aiXi = a2V (Xi) + 2 i aiaj Cov(Xi, Xj ). i i i<j – Typeset by FoilTEX – 13
  15. 15. Variance-Covariance Matrix Σ If the random vector X and the mean µ are defined by       X1 µ1 E(X1) X= .  µ= . . = . . .  Xk µk E(Xk ) then the variance-covariance matrix Σ is defined to be   V (X1) Cov(X1, X2) · · · Cov(X1, Xk )  Cov(X2, X1) V (X2) ··· Cov(X2, Xk )  V (X) =   . . ... .  . . .  Cov(Xk , X1) Cov(Xk , X2) · · · V (Xk ) – Typeset by FoilTEX – 14
  16. 16. Theorem 7. If a is a vector and X is a random vector with mean µ and variance Σ then E(aT X) = aT µ and V (aT X) = aT Σa. If A is a matrix then E(AX) = Aµ and V (AX) = AΣAT – Typeset by FoilTEX – 15
  17. 17. Conditional Expectation Definition 4. The conditional expectation of X given Y = y is xfX|Y (x|y)dx discrete case E(X|Y = y) = xfX|Y (x|y)dx continuous case If r(x, y) is a function of x and y then r(x, y)fX|Y (x|y)dx discrete case E(r(X, Y )|Y = y) = r(x, y)fX|Y (x|y)dx continuous case – Typeset by FoilTEX – 16
  18. 18. • E(X) is a number but E(X|Y = y) is a function of y. • Before observing Y , we don’t know the value of E(X|Y = y) so it is a r.v which we denote E(X|Y ). • E(X|Y ) is the r.v whose value is E(X|Y = y) when Y = y. Theorem 8. For r.vs X and Y , assuming the expectations exist, we have that E[E(Y |X)] = E(Y ) and E[E(X|Y )] = E(X) More generally, for any function r(x, y) we have E[E(r(X, Y )|X)] = E(r(X, Y )) and E[E(r(X, Y )|Y )] = E(r(X, Y )) – Typeset by FoilTEX – 17
  19. 19. Definition 5. The conditional variance is defined as V (Y |X = x) = (y − µ(x))2f (y|x)dy where µ(x) = E(Y |X = x). Theorem 9. For r.vs X and Y , V (Y ) = EV (Y |X) + V E(Y |X). Example 11. Compute the variance of a binomial random variable X with parameters n and p. Example 12. If X and Y are independent random variables both uniformly distributed on (0, 1), then calculate the probability density of X + Y . – Typeset by FoilTEX – 18
  20. 20. Example 13. let X and Y be independent Poisson random variables with respective means λ1 and λ2. Calculate the distribution of X + Y – Typeset by FoilTEX – 19

×