MA 519 Probability: Review

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1 MA 519 : Review Yingwei Wang Department of Mathematics, Purdue University, West Lafayette, IN, USA Contents 1 How to compute the expectation? 1.1 Tail Index Permutations and combinations 3.1 Stars and bars Divided to parts About the Exp(λ) Basic facts Scaling Relation to Geo(p) Memoryless property Hazard rate Comparison between independent variables Two variables with different λ Two variables with the same λ More than two variables with the same λ Order statistics Two variables with joint density n variables with iid Conditional case This is based on the lecture notes of Prof Sellke. 1

2 5 Poisson process Gamma distribution Poisson distribution and binomial distribution Poisson process Definition Comparison Normal Statistics Independent case Dependent case Prediction Limiting distribution The max of Exp(λ) The min of U[0,1] Useful things Jensen Theorem Stirling formula Slutsky Theorem Delta method Central limit theorem Mongolian coins problem Versions Questions How to compute the expectation? 1.1 Tail E(X) P(X k). k1 1. Index E(X) k I k (w), where I k 0 or 1.

3 Permutations and combinations.1 Stars and bars Theorem.1. The number of distinguishable ways that n indistinguishable balls can be distributed among r distinguishable boxes is ( ) n+r 1 r 1 Corollary.1. If box i is requested to have m i balls, ( r i1 m i n), then the answer is ( n r i1 m ) i +r 1 Corollary.. There are box is empty. ( n 1 r 1 r 1 ) ways to distribute n identical balls to r boxes so that no. Divided to parts Theorem.. n people are supposed to be divided into r groups with n r people in each group, i.e. n 1 + +n r n. Then there are ( ways to do that. 3 About the Exp(λ) n n 1,n,,n r ) n! n 1! n r! 3.1 Basic facts If X Exp (λ), then { 0, if t 0, f X (t) λe λt, if t > 0. { 0, if t 0, F X (t) 1 e λt, if t > 0. (3.1) (3.) E(X) 1 λ, E(Xk ) k! λk, (3.3) Var(X) 1 λ. (3.4) 3

4 3. Scaling If X Exp (λ), then cx Exp (λ/c), where c > 0 is a constant. 3.3 Relation to Geo(p) If X Exp (λ), then P(0 < X 1) 1 e λ, P(1 < X ) e λ e λ e λ (1 e λ ), P(k < X k +1) e kλ (1 e λ ). Let P(Y k) P(k < X k +1), then Y Geo (1 e λ ). 3.4 Memoryless property If X Exp (λ), then Note that it is independent of t. 3.5 Hazard rate Definition 3.1. If X Exp (λ), then We call λ as Hazard rate. P(X > t+m X > t) λe λ(t+m) λe λt e λm P(X > m). (3.5) P(X [t,t+δ) X t) P(t < X < t+δ,x t) P(X t) e λ(t+δ) e λt e λt e λδ δλ. Definition 3.. Generally, if random variable T has density function f(t) and cdf F(t), then the Hazard rate is λ T (t) f(t) 1 F(t). 4

5 3.6 Comparison between independent variables Note that all of the random variables here are independent to each other Two variables with different λ If X Exp (λ 1 ), Y Exp (λ ), then Furthermore, let U min(x,y), then P(X < Y) λ 1 λ 1 +λ. (3.6) P(U > t) P(X > t)p(y > t) e (λ 1+λ )t, so U Exp (λ 1 +λ ). (3.7) 3.6. Two variables with the same λ Suppose X,Y Exp (λ), and D X Y,W X Y, then { 1 f D (t) λe λt, if t > 0, 1 λeλt, if t < 0. { 1 f W (t) 3 λeλt, if t < 0, 1 3 λe 1 λt, if t > 0. (3.8) (3.9) Furthermore, U min(x,y), V max(x,y), T V U, then T Exp (λ). Note that T D. Besides, X 1 X 1 +X U[0,1] More than two variables with the same λ Suppose X 1,,X n,y Exp (λ), then min{x 1,,X n } Exp (nλ), (3.10) P(max{X 1,,X n } < Y) 1 n+1, (3.11) ( ) n 1 P(X 1 +X + +X n < Y). (3.1) 5

6 Furthermore, if X 1,,X n, Exp (1), let V n max{x 1,X,,X n }, then consider the order statistics, V n X (n) X (1) +(X () X (1) )+ +(X (n) X (n 1) ), Exp(n)+ Exp(n 1)+ + Exp(1), E(V n ) 1 n + 1 n ln(n), Var(V n ) 1 n + 1 π + (n 1) 6. Remark 3.1. Eq.(3.11) is always true if {X i } and Y are iid, no matter what kind of distribution. Remark 3.. In Eq.(3.1), X 1 +X + +X n Gamma (n,λ). 4 Order statistics 4.1 Two variables with joint density Suppose X,Y have joint density f XY (x,y), then U min(x,y),v max(x,y), then f UV (u,v) { fxy (u,v)+f XY (v,u) u < v, 0 else. (4.1) Furthermore, if we are just interested in the expectation, it is more convenient to use these equations: 4. n variables with iid max(x,y) X +Y min(x,y) X +Y + 1 X Y, (4.) 1 X Y. (4.3) Suppose X 1,X,,X n iid f(t) and F(t), then the density for each X (k) is The combined density is f X(k) Cn 1 Ck 1 n 1 Fk 1 f(1 F) n k, (4.4) n F X(k) Cn j Fj (1 F) n j. (4.5) jk f X(1),X (),,X (n) (x 1,x,,x n ) n!f(x 1 )f(x ) f(x n ), x 1 < x < x n. 6

7 4.3 Conditional case Suppose X 1,X,,X n iid U[0,1], then the combined density of X (1),X (),,X (n) is { n! 0 < x1 < x f X(1),X (1),,X (n) (x 1,x,,x n ) < < x n < 1, (4.6) 0 else. The conditional density of X (1),,X (k 1),X (k+1),x (n) given X (k) a, a [0,1], is f X(1),,X (k 1),X (k+1),,x (n) X (k) a(x 1,,x k 1,x k+1,,x n x k a) { f(x1,,x k 1,a,x k+1,,x n), 0 < x f(x1,,x k 1,a,x k+1,,x n)dx 1 < < x k 1 < a < x k+1 < x n < 1, 0, else. 5 Poisson process 5.1 Gamma distribution Definition 5.1 (Gamma function). Define the function Γ(x) as Γ(x) 0 e y y x 1 dy, x > 0. Remark 5.1. Special case: Γ(n) (n 1)! for n N; Γ(1/) π,γ(1) 1. Definition 5. (Gamma distribution). Say X Gamma(α, λ) if its density is { 0, if t 0, f X (t) λe λt (λt) α 1, if t > 0. Γ(α) Remark 5.. E(X) α λ, E(Xk ) Γ(α+k) λ k Γ(α), Var(X) α λ. 5. Poisson distribution and binomial distribution Definition 5.3 (Poisson distribution). Say X Poisson(λ) if Remark 5.3. E(X) λ, Var(X) λ. P(X k) e λλk k!, k 0,1,. Definition 5.4 (Binomial distribution). Say X Binomial(n, p) if P(X k) C k n pk (1 p) n k, k 0,1,,n. Theorem 5.1. Suppose X Binomial(n,p). If n is very large while p is very small, then X Poisson (λ np). 7

8 5.3 Poisson process Definition Definition 5.5 (Poisson process). Suppose W i Exp(λ), T n n i1 W i, then we say T n is a Poisson process and T n Gamma(n,λ). Theorem 5.. Suppose T n is a Poisson process. Let N the number of W is in the interval [a,b], then N Poisson (λ(b a)) Comparison There are two Poisson processes: A with rate λ A, waiting time X 1,X,,X n ; B with rate λ B : waiting time Y 1,Y,,Y m. Then (I) (II) P(X 1 +X + +X n < Y 1 ) P( First n hits in combined process are all A hits ) ( ) n λa. λ A +λ B P(X 1 +X + +X n +1 < Y 1 ) P( First n hits in combined process are all A hits and no B hits in next 1 time unit ) ( ) n λa e λ B. λ A +λ B (III) P(X 1 +X +X 3 < Y 1 +Y ) P( 3rd A hit are before nd B hit ) P( there are 3 or 4 hits of the first 4 hits are A hits ) ( ) 3 ( ) ( ) 4 λa λb λa. C 3 4 λ A +λ B λ A +λ B +C 4 4 λ A +λ B Remark 5.4. Compare the results here with Section

9 6 Normal Statistics 6.1 Independent case Definition 6.1 (χ distribution ). Say X χ (n) if where x 1,x,,x n iid N(0,1). X x 1 +x + +x n, Remark 6.1. χ (n) Gamma( n, 1 ), χ () Exp( 1 ). Theorem 6.1. Let X 1,X,,X n iid N(µ,σ ), and then 6. Dependent case X 1 n n X i, (6.1) i1 S 1 n 1 n ( Xi X ), (6.) i1 X N(µ, σ ), n (6.3) (n 1)S χ (n 1). σ (6.4) Suppose (X, Y) Standard Bivariate Normal distribution with correlation ρ, then P(X > 0,Y > 0) P(X > 0,ρX + (1 ρ )Z > 0) ρ P(X > 0,Z > Z) 1 ρ ( ) π +arctan ρ 1 ρ where Z N(0,1) and independent with X. π, 9

10 6.3 Prediction Suppose X,Y with correlation ρ, then the prediction of Y based on X is Ŷ ρx, Ŷ µ Y +ρx σ Y, where X X µ X, Y Y µ Y. σ X σ Y 7 Limiting distribution Idea: try to use ( 1 x n) n e x. 7.1 The max of Exp(λ) Suppose X 1,,X n, Exp (1), and V n max{x 1,X,,X n }, then F Vn (t) P(V n < t) (1 e t ) n, (7.1) ) n P(V n ln(n) < t) (1 e (t+ln(n)) ) n (1 e t e e t. (7.) n Now we know that, if n is sufficient large, then E(V n ) ln(n), (7.3) Median (V n ) ln(n) ln(ln()). (7.4) Remark 7.1. Compare the results here with Section The min of U[0,1] Suppose X 1,,X n, U(1), and U n min{x 1,X,,X n }, then F Un (t) 1 P(U n > t) 1 (1 t) n, (7.5) ( F nun (t) 1 P U n > t ) ( 1 1 t n 1 e n n) t. (7.6) It indicates that nu n Exp(1), if n is sufficient large. 10

11 8 Useful things 8.1 Jensen Theorem Theorem 8.1 (Jensen). Suppose Q(x) : R R is convex, then Q(E(X)) E(Q(X)). Remark 8.1. Consider Q(x) x, then E(X ) (EX) Var(X) Stirling formula 8.3 Slutsky Theorem n! ( n ) ne πn θ 1n, θ (0,1). (8.1) e Theorem 8. (Slutsky). If X n X,(D) and Y n a,(p), W n b,(p), then 8.4 Delta method Y n X n +W n ax +b,(d). Suppose a n, a n (W n b) X,(D). Let g : R R be differentiable at b, then 8.5 Central limit theorem a n (g(w n ) g(b)) g (b)x,(d). Theorem 8.3 (Central limit). Suppose X 1,X,,X n iid with mean µ and variance σ. Then X 1 +X + +X n nµ σ n N(0,1). 9 Mongolian coins problem 9.1 Versions Suppose in each toss, P( Head ) θ, where θ U[0,1]. Let N the number of Heads in n tosses. Version I : P(N k) 1 0 C k nθ k (1 θ) n k dθ. 11

12 Recall the order statistics: is the U (k+1) from U 1,,U n+1 iid U[0,1]. So f (k+1) (t) C 1 n+1 Ck n tk (1 t) n k, P(N k) 1 n+1, k. Version II : Let U 0,U 1,,U n iid U[0,1]. Call θ U 0, X k I{U k < U 0 }. Then 9. Questions N n X k the number of U 1,,U n which are < U 0, k1 P(N k) P(U 0 is the U (k+1) from U 0,,U n ) 1 n+1. Given θ, let P(X i 1) P( ith toss get head ), then X 1,X,,X n iid Bernoulli (θ). (i) Compute P(X 3 X 1 X 1). Method one: P(X 3 X 1 X 1) P(X 1 X X 3 1) P(X 1 X 1) E(P(X 1 X X 3 1 θ [0,1])) E(P(X 1 X 1 θ [0,1])) 1 0 θ3 dθ θ dθ Method two: Consider the relative order of U 0,U 1,U,U 3. We can also get the same answer. (ii) Compute P(θ < 1/ X 1 X 1). P(θ < 1/ X 1 X 1) 1/ 0 θ dθ 1 0 θ dθ

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