WRITING THE SHORT STORY - UCLA Extension: UCLA Extension - Higher

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Lecture 4: Random Variables 1. Definition of Random variables 1.1 Measurable functions and random variables 1.2 Reduction of the measurability condition 1.3 Transformation of random variables 1.4 σ-algebra generated by a random variable 2. Distribution functions of random variables 2.1 Measure generated by a random variable 2.2 Distribution function of a random variable 2.3 Properties of distribution functions 2.4 Random variables with a given distribution function 2.5 Distribution functions of transformed random variables 3. Types of distribution functions 3.1 Discrete distribution functions 3.2 Absolutely continuous distribution functions 3.3 Singular distribution functions 3.4 Decomposition representation for distribution functions 4 Multivariate random variables (random vectors) 4.1 Random variables with values in a measurable space 4.2 Random vectors 4.3 Multivariate distribution functions 1

Transcript of WRITING THE SHORT STORY - UCLA Extension: UCLA Extension - Higher

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Lecture 4: Random Variables1. Definition of Random variables

1.1 Measurable functions and random variables1.2 Reduction of the measurability condition1.3 Transformation of random variables1.4 σ-algebra generated by a random variable

2. Distribution functions of random variables

2.1 Measure generated by a random variable2.2 Distribution function of a random variable2.3 Properties of distribution functions2.4 Random variables with a given distribution function2.5 Distribution functions of transformed random variables

3. Types of distribution functions

3.1 Discrete distribution functions3.2 Absolutely continuous distribution functions3.3 Singular distribution functions3.4 Decomposition representation for distribution functions

4 Multivariate random variables (random vectors)

4.1 Random variables with values in a measurable space4.2 Random vectors4.3 Multivariate distribution functions

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5 Independent random variables

5.1 Independent random variables5.2 Mutually independent random variables

1. Definition of Random variables

1.1 Measurable functions and random variables

< Y ,BY > and < X ,BX > are two measurable spaces (spaceplus σ-algebra of measurable subsets of this space);f = f(y) : Y → X is a function acting from Y to X .

Definition 4.1, f = f(y) is a measurable function if

y : f(y) ∈ A ∈ BY , A ∈ BX .

Example

(1) Y = y1, . . . , is a finite or countable set; BY is the σ-algebraof all subsets of Y . In this case, any function f(x) acting fromY to X is measurable.

(2) Y = X = R1; BY = BX = B1 is a Borel σ-algebra. In thiscase, f(x) is called a Borel function.

(3) A continuous function f = f(x) : R1 → R1 is a Borel func-tion.

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< Ω,F , P > is a probability space;X,BX is a measurable space.X = X(ω) : Ω→ X (a function acting from Ω to X ).

Definition 4.2. X = X(ω) is a random variable with valuesin space X defined on a probability space < Ω,F , P > if it is ameasurable function acting from Ω→ X , i.e., such function that

ω : X(ω) ∈ A ∈ F , A ∈ BX .

< Ω,F , P > is a probability space;X = X(ω) : Ω→ R1 (a function acting from Ω to R1);BX = B1 is a Borel σ-algebra of subsets of R1.

Definition 4.3. X = X(ω) is a (real-valued) random variabledefined on a probability space < Ω,F , P > if it is a measurablefunction acting from Ω→ R1, i.e., such function that

ω : X(ω) ∈ A ∈ F , A ∈ B1.

< Ω,F , P > is a probability space;X = X(ω) : Ω→ [−∞,+∞];B+1 is a Borel σ-algebra of subsets of [−∞,+∞] (minimal σ-

algebra containing all intervals [a, b],−∞ ≤ a ≤ b ≤ +∞);

Definition 4.4. X = X(ω) is a (improper) random variabledefined on a probability space < Ω,F , P > if it is a measurablefunction acting from Ω→ R1, i.e., such function that

ω : X(ω) ∈ A ∈ F , A ∈ B+1 .

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Examples

(1) X = x1, . . . , xN, BX is a σ-algebra of all subsets of X; Xis a random variable with a finite set of values;

(2) X = R1,BX = B1; X is a (real-valued) random variable;

(3) X = Rk,BX = Bk; X is a random vector (random variablewith values in Rk;

(4) X is a metric space, BX is a Borel σ-algebra of subsets ofX; X is a random variable with values in the metric space X.

(5) Ω = ω1, ω2 . . . → a discrete sample space;F = F0 → the σ-algebra of all subsets of Ω;

Any function X = X(ω) : Ω → R1 is a random variable since,in this case, it is automatically a measurable function.

(6) Ω = R1;F = B1 → Borel σ-algebra of subsets of R1;

1.2 Reduction of the measurability condition

The following notations are used:

X−1(A) = X ∈ A = ω : X(ω) ∈ A.

Theorem 4.1. The measurability condition (A) X−1(A) ∈F , A ∈ B1 hold if and only if (B) Ax = ω : X(ω) ≤ x ∈F , x ∈ R1.

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———————————(A) ⇒ (B). Indeed, (−∞, x], x ∈ R1 are Borel sets;

(B)⇒ (A). Indeed, let K be a class of all sets A ⊂ R1 such thatX−1(A) ∈ F . Then

(a) X−1((a, b]) = Ab \ Aa ∈ F , a ≤ b. Thus, (a, b] ∈ K, a < b;

(b) A ∈ K ⇒ A ∈ K since X−1(A) = X−1(A) ∈ F ;

(c) A1, A2 . . . ∈ K ⇒ ∪nAn ∈ K since X−1(∪nAn) = ∪nX−1(An)∈ F ;

(d) Thus K is a σ-algebra which contains all intervals. There-fore, B1 ⊆ K.———————————

1.3 Transformation of random variables

X1, . . . , Xk → random variables defined on a probability space< Ω,F , P >;f(x1, . . . , xk) : Rk → R1 → a Borel function, i.e.,

f−1(A) = (x1, . . . , xk) ∈ A ∈ Bk, A ∈ B1.

Theorem 4.2. X = f(X1, . . . , Xk) is a random variable.

———————————(a) ω : X1(ω) ∈ (a1, b1], . . . , Xk(ω) ∈ (ak, bk] ∈ F , ai ≤ bi, i =1, . . . , k;

(b) Let K be a class of all sets A ⊂ Rk such that(X1(ω), . . . , Xk(ω)) ∈ A ∈ F . Then K is a σ-algebra. The

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proof is analogous to those given for Theorem 1.

(c) C ∈ B1 ⇒ ω : f(X1(ω), . . . , Xk(ω)) ∈ C= ω : (X1(ω), . . . , Xk(ω)) ∈ f−1(C) ∈ F .

(d) Thus, X = f(X1, . . . , Xk) is a random variable.———————————

(1) U± = X1 ± X2, V = X1X2, W = X1/X2 (if X2 6= 0) arerandom variables;(2) Z+ = max(X1, . . . , Xk), Z− = min(X1, . . . , Xk) are randomvariables.

X,X1, X2, . . . , are random variables defined on a probabilityspace < Ω,F , P >;

Theorem 4.3. Let X be a random variable defining by one ofthe relation,

X = supn≥1

Xn

X = infn≥1

Xn

X = limn→∞Xn = infn≥1

supk≥n

Xk

X = limn→∞Xn = supn≥1

infk≥n

Xk

X = limn→∞Xn = limn→∞Xn = limn→∞Xn

Then X is a random variable (possibly improper).

———————————(1) ω : supn≥1Xn(ω) > x = ∪n≥1Xn(ω) > x;(2) ω : infn≥1Xn(ω) < x = ∪n≥1Xn(ω) < x;

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(3) ω : limn→∞Xn(ω) < x = ∪l≥1 ∪n≥1 ∩k≥nXk(ω) < x− 1l ;

(4) ω : limn→∞Xn(ω) > x = ∪l≥1 ∪n≥1 ∩k≥nXk(ω) > x− 1l ;

———————————

Let A1, . . . , An ∈ F and a1, . . . , an are real numbers.

Definition 4.5. X(ω) =∑n

k=1 akIAk(ω) is a simple random vari-

able.

Theorem 4.4. X = X(ω) is a random variable if and only ifX(ω) = limn→∞Xn(ω), ω ∈ Ω, where Xn, n = 1, 2, . . . are simplerandom variables.

< Z,BZ >, < Y ,BY > and < X ,BX > and are three measur-able spaces;f = f(z) : Z → Y is a measurable function acting from Z to Y .g = g(y) : Y → X is a measurable function acting from Y to X .

Theorem 4.5. The superposition h(z) = g(f(x)) of two mea-surable functions f and g is a measurable function acting fromspace Z to space X .

———————————(1) Let A ⊆ X . Then h−1(A) = f−1(g−1(A)).(2) Let A ∈ BX . Then g−1(A) ∈ BY ;(3) Then h−1(A) = f−1(g−1(A)) ∈ BZ .———————————

1.4 σ-algebra generated by a random variable

Theorem 4.6 Let X = X(ω) be a random variable defined

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on a probability space < Ω,F , P >. The class of sets FX =<X−1(A), A ∈ B1 > is a σ-algebra (generated by the random vari-able X).

———————————(a) C ∈ FX ⇔ C = X−1(A), where A ∈ B1 ⇒ C = X−1(A) =X−1(A) ∈ FX since A ∈ B1;(b) C1, C2, . . . ∈ FX ⇔ Cn = X−1(An), n = 1, 2, . . ., where An ∈B1, n = 1, 2, . . . ⇒ ∪nCn = ∪nX−1(An) = X−1(∪nAn) ∈ FX

since ∪nAn ∈ B1;(d) Thus FX is a σ-algebra.———————————(1) FX ⊆ F .

Theorem 4.7 Let X = X(ω) be a random variable definedon a probability space < Ω,F , P > and taking values in aspace X with σ-algebra of measurable sets BX . The class ofsets FX =< X−1(A), A ∈ BX > is a σ-algebra (generated by therandom variable X).

2. Distribution functions of random variables

2.1 Measure generated by a random variable

X = X(ω)→ a random variable defined on a probability space< Ω,F , P >.

PX(A) = P (ω : X(ω) ∈ A) = P (X−1(A)), A ∈ B1.

Theorem 4.8. PX(A) is a probability measure defined on Borelσ-algebra B1.

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———————————(a) PX(A) ≥ 0;

(b) A1, A2, . . . ∈ B1, Ai ∩ Aj = ∅ ⇒ X−1(∪nAn) = ∪nX−1(An)and, therefore, PX(∪nAn) = P (X−1(∪nAn)) =

∑n P (X−1(An)) =∑

n PX(An);

(c) PX(R1) = P (X−1(R1)) = P (Ω) = 1.

———————————

Definition 4.6. The probability measure PX(A) is called a dis-tribution of the random variable X.

X = X(ω)→ a random variable defined on a probability space< Ω,F , P > and taking values in a space X with σ-algebra ofmeasurable sets BX .

PX(A) = P (ω : X(ω) ∈ A) = P (X−1(A)), A ∈ BX .

Theorem 4.9. PX(A) is a probability measure defined on Borelσ-algebra BX .

Definition 4.7. The probability measure PX(A) is called a dis-tribution of the random variable X.

2.2 Distribution function of a random variable

X = X(ω)→ a random variable defined on a probability space< Ω,F , P >;PX(A) = P (X(ω) ∈ A) = P (X−1(A)), A ∈ B1 → the distribu-tion of the random variable X.

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Definition 4.8 . The function FX(x) = PX((−∞, x]), x ∈ R1

is called the distribution function of a random variable X.

(1) The distribution PX(A) uniquely determines the distri-bution function FX(x) and, as follows from the continuationtheorem, the distribution function of random variable uniquelydetermines the distribution PX(A).

2.3 Properties of distribution functions

A distribution function FX(x) of a random variable X pos-sesses the following properties:

(1) FX(x) is non-decreasing function in x ∈ R1;

(2) FX(−∞) = limx→−∞ FX(x) = 0, FX(∞) = limx→∞ FX(x)= 1;(3) FX(x) is continuous from the right function, i.e., FX(x) =

limy≥x,y→x FX(y), x ∈ R1.

——————————-(a) x′ ≤ x′′ ⇒ (−∞, x′] ⊆ (−∞, x′′]⇒ FX(x′) = PX((−∞, x′]) ≤FX(x′′) = PX((−∞, x′′]);(b) xn → −∞⇒ zn = maxk≥n xk, ↓ −∞ ⇒ FX(xn) ≤ FX(zn) =PX((−∞, zn])→ 0 since ∩n(−∞, zn] = ∅;(c) xn → ∞ ⇒ zn = mink≥n xk, ↑ ∞ ⇒ FX(xn) ≥ FX(zn) =PX((−∞, zn])→ 1 since ∪n(−∞, zn] = R1;

(d) xn ≥ x, xn → x ⇒ zn = maxk≥n xk, ↓ x ⇒ FX(xn) ≤FX(zn) = PX((−∞, zn]) → FX(x) since ∩n(−∞, zn] = (−∞, x];

——————————-

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(4) P (X ∈ (a, b]) = PX((a, b]) = FX(b)− FX(a), a ≤ b;

(5) P (X = a) = PX(a) = FX(a)− FX(a− 0), a ≤ b;

——————————-(e) PX(a) = limn→∞(FX(a)−FX(a− 1

n)) = FX(a)−FX(a−0)since ∩n(a− 1

n , a] = a.——————————-

(6) Any distribution function has not more that n jumps withvalues ≥ 1

n for every n = 1, 2, . . . and, therefore, the set of alljumps is at most countable.——————————-(f) Let a1 < · · · < aN be some points of jumps with values≥ 1

n . Then ∪Nn=1X = an ⊆ −∞ < X < ∞. Thus, N/n ≤∑Nn=1 P (X = an) = P (∪Nn=1X = an ≤ P (−∞ < X < ∞) = 1

and thus N ≤ n.——————————-

2.4 Random variables with a given distribution func-tion

One can call any function F (x) defined on R1 a distributionfunction if it possesses properties (1)– (3).

According the continuation theorem every distribution func-tion uniquely determines (generates) a probability measure P (A)on B1 which is connected with this distribution function by therelation

P ((a, b]) = F (b)− F (a), a < b.

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Theorem 4.10. For any distribution function F (x) there ex-ists a random variable X that has the distribution functionFX(x) ≡ F (x).

——————————-(a) Choose the probability space < Ω = R1,F = B1, P (A) >where P (A) is the probability measure which is generated bythe distribution function F (x).(b) Consider the random variable X(ω) = ω, ω ∈ R1. ThenP (ω : X(ω) = ω ≤ x) = P ((−∞, x]) = F (x), x ∈ R1.——————————-

Let F (x) is distribution function. One can define the ”in-verse” function

F−1(y) = inf(x : F (x) ≥ y), 0 ≤ y ≤ 1.

.Random variable Y has an uniform distribution if it has the

following distribution function

FY (x) =

0 if x < 0,x if x ∈ [0, 1],1 if x > 1.

Theorem 4.11*. For any distribution function F (x) the ran-dom variable X = F−1(Y ), where Y is a uniformly distributedrandom variable, has the distribution function F (x).——————————-(a) Consider here only the case where F (x) is a continuous

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strictly monotonic distribution function. In this case F−1(y) =inf(y : F (x) = y) is also a continuous strictly monotonic func-tion and F−1(F (x)) = x.(b) F (x) = P (Y ≤ F (x)) = P (F−1(Y ) ≤ F−1(F (x))= P (X ≤ x), x ∈ R1.——————————-

Example

Let F (x) = 1− e−ax, x ≥ 0 be an exponential distribution func-tion In this case F−1(y) = −1

a ln(1 − y) and random variableX = −1

a ln(1 − Y ) has the exponential distribution functionwith parameter a.

2.5 Distribution functions of transformed random vari-ables

X → random variable with a distribution function FX(x) andthe corresponding distribution PX(A);f(x) : R1 → R1 is a Borel function.Af(x) = y ∈ R1 : f(y) ≤ x, x ∈ R1.

Theorem 4.12. The distribution function of the transformedrandom variable Y = f(X) is given by the following formula,

FY (x) = P (f(X) ≤ x) = P (X ∈ Af(x)) = PX(Af(x)), x ∈ R1.

Examples

(1) Y = aX + b, a > 0;Af(x) = (−∞, x−ba ];

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FY (x) = FX(x−ba ).

(2) Y = eaX , a > 0;Af(x) = ∅ if x ≤ 0 or (−∞, 1a lnx] if x > 0;FY (x) = I(x > 0)FX(1a lnx).

(3) Y = X2;Af(x) = ∅ if x ≤ 0 or [−

√x,√x] if x > 0;

FY (x) = I(x > 0)(FX(√x)− FX(−

√x− 0)).

3 Types of distribution functions

3.1 Discrete distribution functions

Definition 4.9. A distribution function F (x) is discrete if thereexists a finite or countable set of points A = a1, a2, . . . suchthat

∑n(F (an)− F (an − 0)) = 1.

If X is a random variable with the distribution function F (x)then

P (X ∈ A) =∑

an∈AP (X = an) =

∑an∈A

(F (an)− F (an − 0)).

Examples

(a) Bernoulli distribution;(b) Discrete uniform distribution;(c) Binomial distribution;(d) Poisson distribution;(e) Geometric distribution;

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3.2 Absolutely continuous distribution functions.

Definition 4.10. A distribution function F (x) is absolutelycontinuous if it can be represented in the following form

F (x) =∫ x

−∞f(y)dy, x ∈ R1,

where (a) f(y) is a Borel non-negative function; (b)∫∞−∞ f(y)dy =

1; (c) Lebesgue integration is used in the formula above (if f(y)is a Riemann integrable function then the Lebesgue integrationcan be replaced by Riemann integration).

Examples

(a) Uniform distribution;(b) Exponential distribution;(c) Normal (Gaussian) distribution;(d) Gamma distribution distribution;(e) Guachy distribution;(f) Pareto distribution.

3.3 Singular distribution functions.

Definition 4.11. A distribution function F (x) is singular if itis a continuous function and its set of points of growth SF hasLebesgue measure m(SF ) = 0 (x is point of growth if F (x+ ε)−F (x− ε) > 0 for any ε > 0).

Example*

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Define a continuous distribution function F (x) such that F (x)= 0 for x < 0 and F (x) = 1 for x > 1, which set of of points ofgrowth SF is the Cantor set, in the following way:

(a) Define function F (x) at the Cantor set in the following way:(1) [0, 1] = [0, 13 ] ∪ [13 ,

23 ] ∪ [23 , 1]: F (x) = 1

2 , x ∈ [13 ,23 ];

(2) [0, 13 ] = [0, 19 ] ∪ [19 ,29 ] ∪ [29 ,

13 ]: F (x) = 1

4 , x ∈ [19 ,29 ];

(3) [23 , 1] = [23 ,79 ] ∪ [79 ,

89 ] ∪ [89 , 1]: F (x) = 3

4 , x ∈ [79 ,89 ];

.......

(b) Define a function F (x) as continuous function in points thatdo not belong to the listed above internal intervals.

(c) The sum of length of all internal intervals, where the func-tion F (x) take constant values is equal

1

3+ 2 · 1

9+ 4 · 1

27+ · · · = 1

3

∞∑k=0

(2

3)k =

1

3· 1

1− 23

= 1.

3.4 Decomposition representation for distribution func-tions

Theorem 4.13 (Lebesgue)**. Any distribution functionF (x) can be represented in the form F (x) = p1F1(x)+p2F2(x)+p3F3(x), x ∈ R1 where (a) F1(x) is a discrete distribution func-tion, (a) F2(x) is an absolutely continuous distribution func-tion, (c) F3(x) is singular distribution function, (d) p1, p2, p2 ≥0, p1 + p2 + p3 = 1.

4 Multivariate random variables (random vectors)

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4.1 Random variables with values in a measurablespace

Let X is an arbitrary space and B(X ) is a σ-algebra of mea-surable subsets of X .

Definition 4.12. A random variable X = X(ω) defined on aprobability space < Ω,F , P > and taking values in the spaceX (with a σ-algebra of measurable subsets B(X )) is a mea-surable function acting from Ω → X , i.e., such function thatω : X(ω) ∈ A ∈ F for any A ∈ B(X ).

Examples

(1) X = R1,B(X ) = B1. In this case, X is a real-valued randomvariable;

(2) X = 0, 1 × · · · × 0, 1 (the product is taken n times),B(X ) is a σ-algebra of all subsets of X . A random variableX = (X1, . . . , Xn) is a Bernoulli vector which components areBernoulli random variables.

(3) X ia a metric space, B(X ) is a Borel σ-algebra of subsets ofX (the minimal σ-algebra containing all balls); X is a randomvariable taking values in the metric space X .

4.2 Random vectors

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X = Rk,B = Bk and P is a probability measure defined onBk.

Definition 4.13. A multivariate random variable (random vec-tor) is a random variable X = (X1, . . . , Xn) defined on a prob-ability space < Ω,F , P > and taking values taking values inthe space X = Rk (with a σ-algebra of measurable subsetsB(X ) = Bk).

(1) Every component of a random vector is a real-valued ran-dom variable defined on the same probability space.

(2) If Xk = Xk(ω), k = 1, . . . , n are real-valued random vari-ables defined on some probability space, then X = (X1, . . . , Xn)is a random vector defined on the same probability space.

4.3 Multivariate distribution functions

Definition. A multivariate distribution function of a ran-dom variable (random vector) X = (X1, . . . , Xn) is a functionF (x1, . . . , xn) defined for x = (x1, . . . , xn) ∈ Rn by the followingrelation

FX1,...,Xn(x1, . . . , xn) = P (X1 ≤ x1, . . . , Xn ≤ xn).

The multivariate distribution function possesses the follow-ing properties:

(1) limxk→−∞ FX1,...,Xn(x1, . . . , xn) = 0;

(2) FX1,...,Xn(x1, . . . , xn) non-decrease in every argument;

(3) limxk→∞,k=1,...,n FX1,...,Xn(x1, . . . , xn) = 1;

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(4) the multivariate distribution functions of the random vectors(X1, . . . , Xn) and (X1, . . . , Xk−1, Xk+1, . . . , Xn) are connected bythe following relation limxk→∞ FX1,...,Xn

(x1, . . . , xn) =FX1,...,Xk−1,Xk+1,...Xn

(x1, . . . , xk−1, xk+1, . . . , xn);

(5) P (X1 ∈ (a1, b1], . . . , Xn ∈ (a1, b1]) = FX1,...,Xn(b1, . . . , bn) −∑

k FX1,...Xn(b1, . . . , bk−1, ak, bk, . . . , bn) + · · ·

+(−1)nFX1,...,Xn(a1, . . . , an) ≥ 0;

(6) FX1,...,Xn(x1, . . . , xn) is continuous from above functions that

is limyk↓xk,k=1,...,n FX1,...,Xn(y1, . . . , yn) = FX1,...,Xn

(x1, . . . , xn).

Example

Let X = (X1, X2) is a two-dimensional random vector. Then

P (X1 ∈ (a1, b1], X2 ∈ (a2, b2]) = FX1,X2(b1, b2)

−FX1,X2(b1, a2)− FX1,X2

(a1, b2) + FX1,X2(a1, a2).

Theorem 4.13. A multivariate distribution functionFX1,...,Xn

(x1, . . . , xn) of a random vector X = (X1, . . . , Xn) uni-quely determines a probability measure PX(A) on the Borel σ-algebra Bk by its values on the cubes PX((a1, b1]×· · ·× (an, bn])= P (X1 ∈ (a1, b1], . . . , Xn ∈ (a1, b1]).

5 Independent random variables

5.1 Independent random variables

Definition Two random variables X and Y with distribu-tion functions, respectively, FX(x) and FY (y) are independent

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if the two-dimensional distribution of the random vector (X, Y )satisfies the relation

FX,Y (x, y) = FX(x) · FY (y), x, y ∈ R1.

(1) If random variables X and Y are independent then P (X ∈A, Y ∈ B) = P (X ∈ A) · P (Y ∈ B) for any A,B ∈ B1.

5.2 Mutually independent random variables

Definition 4.14. Random variables Xt, t ∈ T with distributionfunctions FXt

(x) are mutially independent if for any t1, . . . , tn, ti 6=tj the multivariate distribution function FXt1

,...,Xtn(x1, . . . , xn) of

the random vector (Xt1, . . . , Xtn) satisfies the relation

FXt1,...,Xtn

(x1, . . . , xn) = FXt1(x1)× · · · × FXtn

(xn).

LN Problems

1. LetA is a random event for a probability space< Ω,F , P >

and I = IA(ω) is a indicator of event A. Prove that I is a ran-dom variable.

2. Let X1, X2, . . . be a sequence of random variables definedon a probability space < Ω,F , P >. Let also Z = maxn≥1Xn

and I is an indicator of event A = Z < ∞. Let Y = Z · Iwhere the product is counted as 0 if Z = ∞, I = 0. Prove thatY is a random variable.

3. Let F (x) is a distribution function af a random vari-able X. Prove that P (a ≤ X ≤ b) = F (b) − F (a − 0) and

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P (a < X < b) = F (b− 0)− F (a).

4. Let random variable X has a continuous strictly mono-tonic distribution function F (x). Prove that the random vari-able Y = F (X) is uniformly distributed in the interval [0, 1].

5. Let Ω = ω1, ω2 . . . → be a discrete sample space, F0 →the σ-algebra of all subsets of Ω, and P (A) is a probability mea-sure on F . Let also X is a random variable defined on thediscrete probability space < Ω,F0, P >. Can the random vari-able X be a continuous or a singular distribution function?

5. Let random variable X has a distribution function F (x).What distribution function have random variables Y = aX2 +bX + c?

6 Let X and Y are independent random variables uniformlydistributed in the interval [0, 1]. What is the two-variate distri-bution function of the random vector Z = (X, Y )?

7. Let X1, . . . , Xn be independent random variables withthe same distribution functionF (x). What are the distribu-tion functions of random variables Z+

n = max(X1, . . . , Xn) andZ−n = min(X1, . . . , Xn)?

8. Give the proof of Theorem 4.7.

9. Give the proof of Theorem 4.9.

10. Give the proof of Theorem 4.9 for the case of a general

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distribution function (with possible jumps) [see G]

11. Give the proof of related to the example given in Section3.3.

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