an introduction to probability theory - geiss

8/4/2019 An Introduction to Probability Theory - Geiss

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An introduction to probability theory

Christel Geiss and Stefan Geiss

February 19, 2004


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Contents

1 Probability spaces 7

1.1 Definition of-algebras . . . . . . . . . . . . . . . . . . . . . . 81.2 Probability measures . . . . . . . . . . . . . . . . . . . . . . . 12

1.3 Examples of distributions . . . . . . . . . . . . . . . . . . . . 201.3.1 Binomial distribution with parameter 0 < p < 1 . . . . 201.3.2 Poisson distribution with parameter > 0 . . . . . . . 211.3.3 Geometric distribution with parameter 0 < p < 1 . . . 211.3.4 Lebesgue measure and uniform distribution . . . . . . 211.3.5 Gaussian distribution on

Rwith mean m

Rand

variance 2 > 0 . . . . . . . . . . . . . . . . . . . . . . 221.3.6 Exponential distribution on

R

with parameter > 0 . 221.3.7 Poissons Theorem . . . . . . . . . . . . . . . . . . . . 24

1.4 A set which is not a Borel set . . . . . . . . . . . . . . . . . . 25

2 Random variables 29

2.1 Random variables . . . . . . . . . . . . . . . . . . . . . . . . . 292.2 Measurable maps . . . . . . . . . . . . . . . . . . . . . . . . . 312.3 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3 Integration 39

3.1 Definition of the expected value . . . . . . . . . . . . . . . . . 393.2 Basic properties of the expected value . . . . . . . . . . . . . . 423.3 Connections to the Riemann-integral . . . . . . . . . . . . . . 483.4 Change of variables in the expected value . . . . . . . . . . . . 49

3.5 Fubinis Theorem . . . . . . . . . . . . . . . . . . . . . . . . . 513.6 Some inequalities . . . . . . . . . . . . . . . . . . . . . . . . . 58

4 Modes of convergence 63

4.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.2 Some applications . . . . . . . . . . . . . . . . . . . . . . . . . 64

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4 CONTENTS


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Introduction

The modern period of probability theory is connected with names like S.N.Bernstein (1880-1968), E. Borel (1871-1956), and A.N. Kolmogorov (1903-1987). In particular, in 1933 A.N. Kolmogorov published his modern ap-proach of Probability Theory, including the notion of a measurable spaceand a probability space. This lecture will start from this notion, to continuewith random variables and basic parts of integration theory, and to finishwith some first limit theorems.The lecture is based on a mathematical axiomatic approach and is intendedfor students from mathematics, but also for other students who need moremathematical background for their further studies. We assume that theintegration with respect to the Riemann-integral on the real line is known.The approach, we follow, seems to be in the beginning more difficult. Butonce one has a solid basis, many things will be easier and more transparentlater. Let us start with an introducing example leading us to a problem

which should motivate our axiomatic approach.

Example. We would like to measure the temperature outside our home.We can do this by an electronic thermometer which consists of a sensoroutside and a display, including some electronics, inside. The number we getfrom the system is not correct because of several reasons. For instance, thecalibration of the thermometer might not be correct, the quality of the power-supply and the inside temperature might have some impact on the electronics.It is impossible to describe all these sources of uncertainty explicitly. Henceone is using probability. What is the idea?

Let us denote the exact temperature by T and the displayed temperatureby S, so that the difference T S is influenced by the above sources ofuncertainty. If we would measure simultaneously, by using thermometers ofthe same type, we would get values S1, S2, ... with corresponding differences

D1 := T S1, D2 := T S2, D3 := T S3,...

Intuitively, we get random numbers D1, D2,... having a certain distribution.How to develop an exact mathematical theory out of this?

Firstly, we take an abstract set . Each element will stand for aspecific configuration of our outer sources influencing the measured value.

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6 CONTENTS

Secondly, we take a function

f : R

which gives for all the difference f() = T S. From properties of thisfunction we would like to get useful information of our thermometer and, inparticular, about the correctness of the displayed values. So far, the thingsare purely abstract and at the same time vague, so that one might wonder ifthis could be helpful. Hence let us go ahead with the following questions:

Step 1: How to model the randomness of , or how likely an is? We dothis by introducing the probability spaces in Chapter 1.

Step 2: What mathematical properties of f we need to transport the ran-domness from to f()? This yields to the introduction of the random

variables in Chapter 2.Step 3: What are properties of f which might be important to know inpractice? For example the mean-value and the variance, denoted by

Ef and

E(f

Ef)2.

If the first expression is 0, then the calibration of the thermometer is right,if the second one is small the displayed values are very likely close to the realtemperature. To define these quantities one needs the integration theorydeveloped in Chapter 3.

Step 4: Is it possible to describe the distributions the values of f may take?Or before, what do we mean by a distribution? Some basic distributions arediscussed in Section 1.3.

Step 5: What is a good method to estimateE

f? We can take a sequence ofindependent(take this intuitive for the moment) random variables f1, f2,...,having the same distribution as f, and expect that

1

n

ni=1

fi() and E f

are close to each other. This yields us to the strong law of large numbersdiscussed in Section 4.2.

Notation. Given a set and subsets A, B , then the following notationis used:

intersection: A B = { : A and B}union: A B = { : A or (or both) B}

set-theoretical minus: A\B = { : A and B}complement: Ac = { : A}

empty set: = set, without any elementreal numbers:

R

natural numbers:N

= {1, 2, 3,...}rational numbers:

Q

Given real numbers , , we use := min {, }.


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Chapter 1

Probability spaces

In this chapter we introduce the probability space, the fundamental notionof probability theory. A probability space (, F,

P) consists of three compo-

nents.

(1) The elementary events or states which are collected in a non-emptyset .

Example 1.0.1 (a) If we roll a die, then all possible outcomes are thenumbers between 1 and 6. That means

=

{1, 2, 3, 4, 5, 6

}.

(b) If we flip a coin, then we have either heads or tails on top, thatmeans

= {H, T}.If we have two coins, then we would get

= {(H, H), (H, T), (T, H), (T, T)}.

(c) For the lifetime of a bulb in hours we can choose

= [0, ).

(2) A -algebra F, which is the system of observable subsets of . Given and some A F, one can not say which concrete occurs, but onecan decide whether A or A. The sets A Fare called events: anevent A occurs if A and it does not occur if A.

Example 1.0.2 (a) The event the die shows an even number can bedescribed by

A = {2, 4, 6}.

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8 CHAPTER 1. PROBABILITY SPACES

(b) Exactly one of two coins shows heads is modeled by

A =

{(H, T), (T, H)

}.

(c) The bulb works more than 200 hours we express via

A = (200, ).

(3) A measure P , which gives a probability to any event A , thatmeans to all A F.

Example 1.0.3 (a) We assume that all outcomes for rolling a die are

equally likely, that isP

({}) = 16

.

Then

P({2, 4, 6}) = 1

2.

(b) If we assume we have two fair coins, that means they both show headand tail equally likely, the probability that exactly one of two coinsshows head is

P(

{(H, T), (T, H)

}) =

1

2.

(c) The probability of the lifetime of a bulb we will consider at the end ofChapter 1.

For the formal mathematical approach we proceed in two steps: in a firststep we define the -algebras F, here we do not need any measure. In asecond step we introduce the measures.

1.1 Definition of -algebras

The -algebra is a basic tool in probability theory. It is the set the proba-bility measures are defined on. Without this notion it would be impossibleto consider the fundamental Lebesgue measure on the interval [0, 1] or toconsider Gaussian measures, without which many parts of mathematics cannot live.

Definition 1.1.1 [-algebra, algebra, measurable space] Let bea non-empty set. A system Fof subsets A is called -algebra on if

(1) , F,(2) A Fimplies that Ac := \A F,


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1.1. DEFINITION OF -ALGEBRAS 9

(3) A1, A2,... Fimplies that

i=1 Ai F.The pair (,

F), where

Fis a -algebra on , is called measurable space.

If one replaces (3) by

(3) A, B Fimplies that A B F,then Fis called an algebra.

Every -algebra is an algebra. Sometimes, the terms -field and field areused instead of -algebra and algebra. We consider some first examples.

Example 1.1.2 [-algebras]

(a) The largest -algebra on : if F = 2 is the system of all subsetsA , then Fis a -algebra.

(b) The smallest -algebra: F= {, }.(c) If A , then F= {, , A , Ac} is a -algebra.

If = {1,...,n}, then any algebra Fon is automatically a -algebra.However, in general this is not the case. The next example gives an algebra,which is not a -algebra:

Example 1.1.3 [algebra, which is not a -algebra] Let G be thesystem of subsets A

Rsuch that A can be written as

A = (a1, b1] (a2, b2] (an, bn]

where a1 b1 an bn with the convention that(a, ] = (a, ). Then G is an algebra, but not a -algebra.

Unfortunately, most of the important algebras can not be constructed

explicitly. Surprisingly, one can work practically with them nevertheless. Inthe following we describe a simple procedure which generates algebras. Westart with the fundamental

Proposition 1.1.4 [intersection of -algebras is a -algebra] Let be an arbitrary non-empty set and let Fj, j J, J = , be a family of-algebras on , where J is an arbitrary index set. Then

F:=jJ

Fj

is a -algebra as well.


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Proof. The proof is very easy, but typical and fundamental. First we noticethat , Fj for all j J, so that ,

jJFj. Now let A, A1, A2,...

jJFj . Hence A, A1, A2,... Fj for all j J, so that (Fj are algebras!)Ac = \A Fj and

i=1

Ai Fj

for all j J. Consequently,

Ac jJ

Fj andi=1

Ai jJ

Fj.

Proposition 1.1.5 [smallest -algebra containing a set-system]Let be an arbitrary non-empty set andG be an arbitrary system of subsetsA . Then there exists a smallest -algebra (G) on such that

G (G).

Proof. We let

J :=

{Cis a algebra on such that

G C}.

According to Example 1.1.2 one has J = , becauseG 2

and 2 is a algebra. Hence

(G) :=CJ

C

yields to a -algebra according to Proposition 1.1.4 such that (by construc-

tion) G (G). It remains to show that (G) is the smallest -algebracontaining G. Assume another -algebra Fwith G F. By definition of Jwe have that F J so that

(G) =CJ

C F.

The construction is very elegant but has, as already mentioned, the slightdisadvantage that one cannot explicitly construct all elements of (G). Letus now turn to one of the most important examples, the Borel -algebra onR . To do this we need the notion of open and closed sets.


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1.1. DEFINITION OF -ALGEBRAS 11

Definition 1.1.6 [open and closed sets]

(1) A subset A

R

is called open, if for each x

A there is an > 0

such that (x , x + ) A.(2) A subset B

Ris called closed, if A := R \B is open.

It should be noted, that by definition the empty set is open and closed.

Proposition 1.1.7 [Generation of the Borel -algebra on R ] Welet

G0 be the system of all open subsets of R ,G1 be the system of all closed subsets of R ,G2 be the system of all intervals (, b], b R ,G3 be the system of all intervals (, b), b R ,G4 be the system of all intervals (a, b], < a < b < ,G5 be the system of all intervals (a, b), < a < b < .Then (G0) = (G1) = (G2) = (G3) = (G4) = (G5).

Definition 1.1.8 [Borel -algebra onR

] The -algebra constructed inProposition 1.1.7 is called Borel -algebra and denoted by B(R ).

Proof of Proposition 1.1.7. We only show that

(G0) = (G1) = (G3) = (G5).Because ofG3 G0 one has

(G3) (G0).Moreover, for < a < b < one has that

(a, b) =

n=1

(, b)\(, a + 1n) (G3)so that G5 (G3) and

(G5) (G3).Now let us assume a bounded non-empty open set A

R

. For all x Athere is a maximal x > 0 such that

(x x, x + x) A.Hence

A = xAQ

(x x, x + x),


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which proves G0 (G5) and(

G0)

(

G5).

Finally, A G0 implies Ac G1 (G1) and A (G1). Hence G0 (G1)and

(G0) (G1).The remaining inclusion (G1) (G0) can be shown in the same way.

1.2 Probability measures

Now we introduce the measures we are going to use:

Definition 1.2.1 [probability measure, probability space] Let(, F) be a measurable space.

(1) A map : F [0, ] is called measure if () = 0 and for allA1, A2,... Fwith Ai Aj = for i = j one has

i=1

Ai

=

i=1

(Ai). (1.1)

The triplet (, F, ) is called measure space.(2) A measure space (, F, ) or a measure is called -finite provided

that there are k , k = 1, 2,..., such that(a) k Ffor all k = 1, 2,...,(b) i j = for i = j,(c) =

k=1 k,

(d) (k) < .The measure space (,

F, ) or the measure are called finite if

() < .(3) A measure space (, F, ) is called probability space and proba-

bility measure provided that () = 1.

Example 1.2.2 [Dirac and counting measure]

(a) Dirac measure: For F= 2 and a fixed x0 we let

x0(A) := 1 : x0 A0 : x0 A .


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1.2. PROBABILITY MEASURES 13

(b) Counting measure: Let := {1,...,N} and F= 2. Then

(A) := cardinality ofA.

Let us now discuss a typical example in which the algebra Fis not the setof all subsets of .

Example 1.2.3 Assume there are n communication channels between thepoints A and B. Each of the channels has a communication rate of > 0(say bits per second), which yields to the communication rate k, in casek channels are used. Each of the channels fails with probability p, so thatwe have a random communication rate R {0,,...,n}. What is the rightmodel for this? We use

:= { = (1,...,n) : i {0, 1})

with the interpretation: i = 0 if channel i is failing, i = 1 if channel i isworking. Fconsists of all possible unions of

Ak := { : 1 + + n = k} .

Hence Ak consists of all such that the communication rate is k. Thesystem Fis the system of observable sets of events since one can only observehow many channels are failing, but not which channels are failing. The

measure P is given by

P

(Ak) :=

n

k

pnk(1 p)k, 0 < p < 1.

Note thatP

describes the binomial distribution with parameter p on{0,...,n} if we identify Ak with the natural number k.

We continue with some basic properties of a probability measure.

Proposition 1.2.4 Let (,F

,P

) be a probability space. Then the followingassertions are true:

(1) Without assuming thatP

() = 0 the -additivity (1.1) implies thatP

() = 0.(2) If A1,...,An F such that Ai Aj = if i = j, then P (

ni=1 Ai) =n

i=1 P (Ai).

(3) If A, B F, thenP

(A\B) =P

(A) P

(A B).(4) If B , then

P(Bc) = 1

P(B).

(5) If A1, A2,... F then P (i=1 Ai) i=1 P (Ai).


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(6) Continuity from below: If A1, A2,... F such that A1 A2 A3 , then

limnP (An) = P

n=1

An .(7) Continuity from above: If A1, A2,... F such that A1 A2

A3 , thenlimn

P(An) = P

n=1

An

.

Proof. (1) Here one has for An := that

P

() = P n=1

An = n=1

P

(An) =

n=1

P

() ,

so thatP

() = 0 is the only solution.(2) We let An+1 = An+2 = = , so that

P

ni=1

Ai

=

P

i=1

Ai

=

i=1

P

(Ai) =ni=1

P

(Ai) ,

because ofP

() = 0.(3) Since (A

B)

(A

\B) =

, we get that

P(A B) +

P(A\B) =

P((A B) (A\B)) =

P(A).

(4) We apply (3) to A = and observe that \B = Bc by definition and B = B.(5) Put B1 := A1 and Bi := A

c1Ac2 Aci1Ai for i = 2, 3, . . . Obviously,

P(Bi) P (Ai) for all i. Since the Bis are disjoint and

i=1 Ai =

i=1 Bi it

follows

P

i=1

Ai

=

P

i=1

Bi

=

i=1

P(Bi)

i=1

P(Ai).

(6) We define B1 := A1, B2 := A2\A1, B3 := A3\A2, B4 := A4\A3, ... andget that

n=1

Bn =n=1

An and Bi Bj =

for i = j. Consequently,

P

n=1

An

=

P

n=1

Bn

=

n=1

P(Bn) = lim

N

Nn=1

P(Bn) = lim

NP

(AN)

since Nn=1 Bn = AN. (7) is an exercise.


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Definition 1.2.5 [lim infn An and lim supn An] Let (, F) be a measurablespace and A1, A2,... F. Then

lim infn

An :=n=1

k=n

Ak and lim supn

An :=n=1

k=n

Ak.

The definition above says that lim infn An if and only if all events An,except a finite number of them, occur, and that lim supn An if and onlyif infinitely many of the events An occur.

Definition 1.2.6 [lim infn n and lim supn n] For 1, 2,... R we let

lim infn n := limn infkn k and lim supn n := limn supkn

k.

Remark 1.2.7 (1) The value lim infn n is the infimum of all c such thatthere is a subsequence n1 < n2 < n3 < such that limk nk = c.

(2) The value lim supn n is the supremum of all c such that there is asubsequence n1 < n2 < n3 < such that limk nk = c.

(3) By definition one has that

lim infn

n lim supn

n .

(4) For example, taking n = (1)n, gives

lim infn

n = 1 and lim s upn

n = 1.

Proposition 1.2.8 [Lemma of Fatou] Let(, F, P ) be a probability spaceand A1, A2,...

F. Then

P

lim inf

nAn

lim inf

nP

(An) lim supn

P(An) P

lim sup

nAn

.

The proposition will be deduced from Proposition 3.2.6 below.

Definition 1.2.9 [independence of events] Let (, F, P ) be a proba-bility space. The events A1, A2,... F are called independent, providedthat for all n and 1 k1 < k2 < < kn one has that

P

(Ak1 Ak2 Akn) = P (Ak1) P (Ak2) P (Akn) .


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One can easily see that only demanding

P(A1

A2

An) = P (A1) P (A2)

P

(An) .

would not make much sense: taking A and B with

P(A B) =

P(A)

P(B)

and C = givesP

(A B C) =P

(A)P

(B)P

(C),

which is surely not, what we had in mind.

Definition 1.2.10 [conditional probability] Let (, F, P ) be a prob-ability space, A Fwith P (A) > 0. Then

P(B|A) := P (B A)

P(A)

, for B F,

is called conditional probability of B given A.

As a first application let us consider the Bayes formula. Before we formulatethis formula in Proposition 1.2.12 we consider A, B F, with 0 0. Then

A = (A B) (A Bc

),where (A B) (A Bc) = , and therefore,

P(A) =

P(A B) +

P(A Bc)

=P

(A|B)P

(B) +P

(A|Bc)P

(Bc).

This implies

P

(B|A) = P (B A)P

(A)=

P(A|B)

P(B)

P(A)

=P

(A|B)

P(B)

P(A|B)

P(B) +

P(A|Bc)

P(Bc) .

Let us consider an

Example 1.2.11 A laboratory blood test is 95% effective in detecting acertain disease when it is, in fact, present. However, the test also yields afalse positive result for 1% of the healthy persons tested. If 0.5% of thepopulation actually has the disease, what is the probability a person has thedisease given his test result is positive? We set

B := person has the disease,

A := the test result is positive.


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Hence we have

P(A

|B) =

P(a positive test result

|person has the disease) = 0.95,

P(A|Bc) = 0.01,

P(B) = 0.005.

Applying the above formula we get

P(B|A) = 0.95 0.005

0.95 0.005 + 0.01 0.995 0.323.That means only 32% of the persons whose test results are positive actuallyhave the disease.

Proposition 1.2.12 [Bayes formula] Assume A, Bj F, with =nj=1 Bj , with Bi Bj = for i = j and P (A) > 0, P (Bj) > 0 for

j = 1, . . . , n. Then

P(Bj|A) = P (A|Bj) P (Bj)n

k=1 P (A|Bk) P (Bk).

The proof is an exercise.

Proposition 1.2.13 [Lemma of Borel-Cantelli] Let (,

F,

P) be a

probability space and A1, A2,... F. Then one has the following:(1) If

n=1 P (An) < , then P (lim supn An) = 0.

(2) IfA1, A2,... are assumed to be independent and

n=1 P (An) = , thenP

(lim supn An) = 1.

Proof. (1) It holds by definition lim supn An =

n=1

k=n Ak. By

k=n+1 Ak

k=n Akand the continuity of

P

from above (see Proposition 1.2.4) we get

P

lim supn

An

=

P

n=1

k=n

Ak

= limn

P

k=n

Ak

limnk=n

P (Ak) = 0,


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where the last inequality follows from Proposition 1.2.4.

(2) It holds that

lim sup

nAn

c= lim inf

nAcn =

n=1

k=n

Acn.

So, we would need to show that

P

n=1

k=n

Acn

= 0.

Letting Bn :=

k=n Ack we get that B1 B2 B3 , so that

P

n=1

k=n

Ac

n = limn P (Bn)so that it suffices to show that

P

(Bn) = P

k=n

Ack

= 0.

Since the independence of A1, A2,... implies the independence of Ac1, A

c2,...,

we finally get (setting pn := P (An)) that

P

k=n Ack = limN,Nn P N

k=n Ack= lim

N,Nn

Nk=n

P(Ack)

= limN,Nn

Nk=n

(1 pk)

limN,Nn

Nk=n

epn

= limN,Nn eN

k=n pn

= e

k=n pn

= e

= 0

where we have used that 1 x ex for x 0.

Although the definition of a measure is not difficult, to prove existence anduniqueness of measures may sometimes be difficult. The problem lies in thefact that, in general, the -algebras are not constructed explicitly, one only

knows its existence. To overcome this difficulty, one usually exploits


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Proposition 1.2.14 [Caratheodorys extension theorem]Let be a non-empty set and G be an algebra on such that

F:= (G).Assume that

P 0 : G [0, 1] satisfies:(1)

P 0() = 1.

(2) If A1, A2,... F, Ai Aj = for i = j, and

i=1 Ai G, then

P 0

i=1

Ai

=

i=1

P 0(Ai).

Then there exists a unique probability measureP

on Fsuch that

P

(A) =P 0(A) for all A G.

Proof. See [3] (Theorem 3.1).

As an application we construct (more or less without rigorous proof) theproduct space

(1 2, F1 F2, P 1 P 2)

of two probability spaces (1, F1, P 1) and (2, F2, P 2). We do this as follows:(1) 1 2 := {(1, 2) : 1 1, 2 2}.(2) F1 F2 is the smallest -algebra on 1 2 which contains all sets of

type

A1 A2 := {(1, 2) : 1 A1, 2 A2} with A1 F1, A2 F2.

(3) As algebra G we take all sets of type

A := A11 A12 (An1 An2)with Ak1 F1, Ak2 F2, and (Ai1 Ai2)

Aj1 Aj2

= for i = j.

Finally, we define : G [0, 1] by

A11 A12 (An1 An2 ) := n

k=1

P 1(Ak1)P 2(A

k2).

Definition 1.2.15 [product of probability spaces] The extension of to F1F2 according to Proposition 1.2.14 is called product measure andusually denoted by

P

1P

2. The probability space (12, F1F2,P

1P

2)is called product probability space.


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One can prove that

(F

1

F2)

F3 =

F1

(F

2

F3) and (P 1

P 2)

P 3 = P 1

(

P 2

P 3).

Using this approach we define the the Borel -algebra onR

n.

Definition 1.2.16 For n {1, 2,...} we let

B(R

n) := B(R

) B(R

).

There is a more natural approach to define the Borel -algebra onR

n: it isthe smallest -algebra which contains all sets which are open which are openwith respect to the euclidean metric in

R

n. However to be efficient, we havechosen the above one.

If one is only interested in the uniqueness of measures one can also use thefollowing approach as a replacement of Caratheodorys extension theo-rem:

Definition 1.2.17 [-system] A system G of subsets A is called -system, provided that

A B G for all A, B G.

Proposition 1.2.18 Let(, F) be a measurable space withF= (G), whereG is a-system. Assume two probability measures

P 1 and P 2 onFsuch that

P 1(A) = P 2(A) for all A G.

ThenP 1(B) = P 2(B) for all B F.

1.3 Examples of distributions

1.3.1 Binomial distribution with parameter 0 < p < 1

(1) := {0, 1,...,n}.

(2) F:= 2 (system of all subsets of ).

(3)P

(B) = n,p(B) :=n

k=0

nk

pk(1 p)nkk(B), where k is the Dirac

measure introduced in Definition 1.2.2.

Interpretation: Coin-tossing with one coin, such that one has head with

probability p and tail with probability 1 p. Then n,p({k}) is equals theprobability, that within n trials one has k-times head.


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1.3. EXAMPLES OF DISTRIBUTIONS 21

1.3.2 Poisson distribution with parameter > 0

(1) :=

{0, 1, 2, 3,...

}.

(2) F:= 2 (system of all subsets of ).(3)

P(B) = (B) :=

k=0 e

k

k!k(B).

The Poisson distribution is used for example to model jump-diffusion pro-cesses: the probability that one has k jumps between the time-points s andt with 0 s < t < , is equal to (ts)({k}).

1.3.3 Geometric distribution with parameter 0 < p < 1

(1) := {0, 1, 2, 3,...}.(2) F:= 2 (system of all subsets of ).(3)

P(B) = p(B) :=

k=0(1 p)kpk(B).

Interpretation: The probability that an electric light bulb breaks downis p (0, 1). The bulb does not have a memory, that means the breakdown is independent of the time the bulb is already switched on. So, weget the following model: at day 0 the probability of breaking down is p. Ifthe bulb survives day 0, it breaks down again with probability p at the firstday so that the total probability of a break down at day 1 is (1

p)p. If we

continue in this way we get that breaking down at day k has the probability(1 p)kp.

1.3.4 Lebesgue measure and uniform distribution

Using Caratheodorys extension theorem, we shall construct the Lebesguemeasure on compact intervals [a, b] and on

R

. For this purpose we let

(1) := [a, b], < a < b < ,(2)

F=

B([a, b]) :=

{B = A

[a, b] : A

B(

R)

}.

(3) As generating algebra G for B([a, b]) we take the system of subsetsA [a, b] such that A can be written as

A = (a1, b1] (a2, b2] (an, bn]or

A = {a} (a1, b1] (a2, b2] (an, bn]where a a1 b1 an bn b. For such a set A we let

0

i=1(ai, bi] :=

i=1 (bi ai).


22/71


Definition 1.3.1 [Lebesgue measure] The unique extension of 0 toB([a, b]) according to Proposition 1.2.14 is called Lebesgue measure anddenoted by .

We also write (B) =B

d(x). Letting

P(B) :=

1

b a(B) for B B([a, b]),

we obtain the uniform distribution on [a, b]. Moreover, the Lebesguemeasure can be uniquely extended to a -finite measure on B(

R) such that

((a, b]) = b a for all < a < b < .

1.3.5 Gaussian distribution on R with mean m R andvariance 2 > 0

(1) :=R

.

(2) F:= B(R

) Borel -algebra.

(3) We take the algebra G considered in Example 1.1.3 and define

P 0(A) :=n

i=1 bi

ai

1

22e

(xm)2

22 dx

for A := (a1, b1](a2, b2] (an, bn] where we consider the Riemann-integral on the right-hand side. One can show (we do not do this here,but compare with Proposition 3.5.8 below) that

P 0 satisfies the assump-tions of Proposition 1.2.14, so that we can extend

P 0 to a probabilitymeasure Nm,2 on B( R ).

The measure Nm,2 is called Gaussian distribution (normal distribu-tion) with mean m and variance 2. Given A B(R ) we write

Nm,2(A) =A

pm,2(x)dx with pm,2(x) :=1

22e

(xm)2

22 .

The function pm,2(x) is called Gaussian density.

1.3.6 Exponential distribution on R with parameter

> 0

(1) :=R

.

(2) F:= B(R

) Borel -algebra.


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1.3. EXAMPLES OF DISTRIBUTIONS 23

(3) For A and G as in Subsection 1.3.5 we define

P 0(A) :=

ni=1

biai

p(x)dx with p(x) := 1I[0,)(x)ex

Again,P 0 satisfies the assumptions of Proposition 1.2.14, so that we

can extendP 0 to the exponential distribution with parameter

and density p(x) on B(R ).

Given A B(R

) we write

(A) = Ap(x)dx.The exponential distribution can be considered as a continuous time versionof the geometric distribution. In particular, we see that the distribution doesnot have a memory in the sense that for a, b 0 we have

([a + b, )|[a, )) = ([b, )),

where we have on the left-hand side the conditional probability. In words: theprobability of a realization larger or equal to a + b under the condition thatone has already a value larger or equal a is the same as having a realization

larger or equal b. Indeed, it holds

([a + b, )|[a, )) = ([a + b, ) [a, ))([a, ))

=a+b

exdx

a

exdx

=e(a+b)

ea

= ([b, )).

Example 1.3.2 Suppose that the amount of time one spends in a post officeis exponential distributed with = 1

10.

(a) What is the probability, that a customer will spend more than 15 min-utes?

(b) What is the probability, that a customer will spend more than 15 min-utes in the post office, given that she or he is already there for at least10 minutes?

The answer for (a) is ([15,

)) = e15

110

0.220. For (b) we get

([15, )|[10, )) = ([5, )) = e5 110 0.604.


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1.3.7 Poissons Theorem

For large n and small p the Poisson distribution provides a good approxima-

tion for the binomial distribution.

Proposition 1.3.3 [Poissons Theorem] Let > 0, pn (0, 1), n =1, 2,..., and assume that npn as n . Then, for all k = 0, 1, . . . ,

n,pn({k}) ({k}), n .Proof. Fix an integer k 0. Then

n,pn({k}) =

n

k

pkn(1 pn)nk

= n(n 1) . . . (n k + 1)k! pkn(1 pn)nk

=1

k!

n(n 1) . . . (n k + 1)nk

(npn)k (1 pn)nk.

Of course, limn(npn)k = k and limn

n(n1)...(nk+1)nk

= 1. So we haveto show that limn(1 pn)nk = e. By npn we get that there existn such that

npn = + n with limn

n = 0.

Choose 0 > 0 and n0

1 such that|n

| 0 for all n

n0. Then

1 + 0n

nk

1 + nn

nk

1 0n

nk.

Using lHospitals rule we get

limn

ln

1 + 0

n

nk= lim

n(n k) ln

1 + 0

n

= lim

n

ln

1 +0n

1/(n k)

= limn

1 +0n 1 +0n21/(n k)2

= ( + 0).Hence

e(+0) = limn

1 + 0

n

nk lim

n

1 + n

n

nk.

In the same way we get

limn

1 + nn

nk e(0).


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1.4. A SET WHICH IS NOT A BOREL SET 25

Finally, since we can choose 0 > 0 arbitrarily small

limn(1 pn)nk

= limn1 + nn nk

= e

.

1.4 A set which is not a Borel set

In this section we shall construct a set which is a subset of (0, 1] but not anelement of

B((0, 1]) := {B = A (0, 1] : A B(R

)} .Before we start we need

Definition 1.4.1 [-system] A class L is a -system if(1) L,(2) A, B L and A B imply B\A L,(3) A1, A2, L and An An+1, n = 1, 2, . . . imply

n=1 An L.

Proposition 1.4.2 [--Theorem] If

Pis a -system and

Lis a -

system, thenP L implies (P) L.

Definition 1.4.3 [equivalence relation] An relation on a set X iscalled equivalence relation if and only if

(1) x x for all x X (reflexivity),(2) x y implies x y for x, y X (symmetry),(3) x y and y z imply x z for x,y,z X (transitivity).

Given x, y (0, 1] and A (0, 1], we also need the addition modulo one

x y :=

x + y if x + y (0, 1]x + y 1 otherwise

andA x := {a x : a A}.

Now define

L:=

{A

B((0, 1]) such that

A x B((0, 1]) and (A x) = (A) for all x (0, 1]}.


26/71


Lemma 1.4.4 L is a -system.Proof. The property (1) is clear since

x = . To check (2) let A, B

Land A B, so that(A x) = (A) and (B x) = (B).

We have to show that B \ A L. By the definition of it is easy to see thatA B implies A x B x and

(B x) \ (A x) = (B \ A) x,and therefore, (B \ A) x B((0, 1]). Since is a probability measure itfollows

(B \ A) = (B) (A)= (B x) (A x)= ((B x) \ (A x))= ((B \ A) x)

and B\A L. Property (3) is left as an exercise. Finally, we need the axiom of choice.

Proposition 1.4.5 [Axiom of choice] Let I be a set and (M)I be a

system of non-empty sets M. Then there is a function on I such that

: m M.In other words, one can form a set by choosing of each set M a representativem.

Proposition 1.4.6 There exists a subset H (0, 1] which does not belongto B((0, 1]).

Proof. If (a, b] [0, 1], then (a, b] L. SinceP:= {(a, b] : 0 a < b 1}

is a -system which generates B((0, 1]) it follows by the --Theorem 1.4.2that

B((0, 1]) L.Let us define the equivalence relation

x y if and only if x r = y for some rational r (0, 1].

Let H (0, 1] be consisting of exactly one representative point from eachequivalence class (such set exists under the assumption of the axiom of


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1.4. A SET WHICH IS NOT A BOREL SET 27

choice). Then H r1 and H r2 are disjoint for r1 = r2: if they werenot disjoint, then there would exist h1 r1 (Hr1) and h2 r2 (H r2)with h1

r1 = h2

r2. But this implies h1

h2 and hence h1 = h2 and

r1 = r2. So it follows that (0, 1] is the countable union of disjoint sets

(0, 1] =

r(0,1] rational

(H r).

If we assume that H B((0, 1]) then

((0, 1]) =

r(0,1] rational

(H r)

=

r(0,1] rational

(H r).

By B((0, 1]) L we have (H r) = (H) = a 0 for all rational numbersr (0, 1]. Consequently,

1 = ((0, 1]) =

r(0,1] rational

(H r) = a + a + . . .

So, the right hand side can either be 0 (ifa = 0) or (if a > 0). This leadsto a contradiction, so H B((0, 1]).


28/71



29/71

Chapter 2

Random variables

Given a probability space (, F, P ), in many stochastic models one considersfunctions f :

R, which describe certain random phenomena, and is

interested in the computation of expressions like

P

({ : f() (a, b)}) , where a < b.This yields us to the condition

{ : f() (a, b)} Fand hence to random variables we introduce now.

2.1 Random variables

We start with the most simple random variables.

Definition 2.1.1 [(measurable) step-function] Let (, F) be a mea-surable space. A function f :

Ris called measurable step-function

or step-function, provided that there are 1,...,n R and A1,...,An Fsuch that f can be written as

f() =n

i=1i1IAi(),

where

1IAi() :=

1 : Ai0 : Ai .

Some particular examples for step-functions are

1I = 1,

1I = 0,

1IA + 1IAc = 1,

1IAB = 1IA1IB,

1IAB = 1IA + 1IB 1IAB.

29


30/71

30 CHAPTER 2. RANDOM VARIABLES

The definition above concerns only functions which take finitely many values,which will be too restrictive in future. So we wish to extend this definition.

Definition 2.1.2 [random variables] Let (, F) be a measurable space.A map f :

R

is called random variable provided that there is asequence (fn)

n=1 of measurable step-functions fn : R such that

f() = limn

fn() for all .

Does our definition give what we would like to have? Yes, as we see from

Proposition 2.1.3 Let (, F) be a measurable space and let f : R bea function. Then the following conditions are equivalent:

(1) f is a random variable.

(2) For all < a < b < one has thatf1((a, b)) := { : a < f() < b} F.

Proof. (1) = (2) Assume thatf() = lim

nfn()

where fn : R are measurable step-functions. For a measurable step-function one has that

f1n ((a, b)) Fso that

f1((a, b)) =

: a < limn

fn() < b

=

m=1

N=1

n=N

: a + 1

m< fn() < b 1

m

F.

(2) = (1) First we observe that we also have thatf1([a, b)) =

{

: a

f() < b

}=

m=1

: a 1

m< f() < b

F

so that we can use the step-functions

fn() :=4n1k=4n

k

2n1I{ k2nf 0,

P

({ : f() }) E

f

.

Proof. We simply have

P

({ : f() }) = E

1I{f} E f1I{f} E f.

Definition 3.6.2 [convexity] A function g : R R is convex if andonly if

g(px + (1 p)y) pg(x) + (1 p)g(y)for all 0 p 1 and all x, y

R.

Every convex function g :R

R

is (B(R

), B(R

))-measurable.

Proposition 3.6.3 [Jensens inequality] If g : R R is convex andf :

Ra random variable with

E|f| < , then

g(E

f) E

g(f)

where the expected value on the right-hand side might be infinity.


59/71

3.6. SOME INEQUALITIES 59

Proof. Let x0 = E f. Since g is convex we find a supporting line, thatmeans a, b

Rsuch that

ax0 + b = g(x0) and ax + b g(x)for all x

R. It follows af() + b g(f()) for all and

g(E

f) = aE

f + b =E

(af + b) E

g(f).

Example 3.6.4 (1) The function g(x) := |x| is convex so that, for anyintegrable f,

|E f| E |f|.(2) For 1 p < the function g(x) := |x|p is convex, so that Jensens

inequality applied to |f| gives that(

E|f|)p

E|f|p.

For the second case in the example above there is another way we can go. Ituses the famous Holder-inequality.

Proposition 3.6.5 [Holders inequality] Assume a probability space

(, F, P ) and random variables f, g : R . If1 < p, q < with 1p + 1q = 1,then

E|f g| (

E|f|p) 1p (

E|g|q) 1q .

Proof. We can assume thatE

|f|p > 0 andE

|g|q > 0. For example, assumingE

|f|p = 0 would imply |f|p = 0 a.s. according to Proposition 3.2.1 so thatf g = 0 a.s. and

E|f g| = 0. Hence we may set

f :=f

(E

|f|p

)

1p

and g :=g

(E

|g|q

)

1q

.

We notice thatxayb ax + by

for x, y 0 and positive a, b with a + b = 1, which follows from the concavityof the logarithm (we can assume for a moment that x, y > 0)

ln(ax + by) a ln x + b ln y = ln xa + ln yb = ln xayb.Setting x := |f|p, y := |g|q, a := 1

p, and b := 1

q, we get

|fg| = xayb ax + by = 1p

|f|p + 1q|g|q


60/71

60 CHAPTER 3. INTEGRATION

and

E|fg| 1

pE

|f|p + 1q

E|g|q = 1

p+

1

q= 1.

On the other hand side,

E|fg| = E |f g|

(E

|f|p) 1p (E

|g|q) 1qso that we are done.

Corollary 3.6.6 For 0 < p < q < one has that (E |f|p) 1p ( E |f|q) 1q .

The proof is an exercise.

Corollary 3.6.7 [Holders inequality for sequences] Let (an)n=1and (bn)

n=1 be sequences of real numbers. Then

n=1

|anbn|

n=1

|an|p 1

p

n=1

|bn|q1

q

.

Proof. It is sufficient to prove the inequality for finite sequences (bn)Nn=1 since

by letting N we get the desired inequality for infinite sequences. Let = {1,...,N}, F := 2, and

P({k}) := 1/N. Defining f, g :

Rby

f(k) := ak and g(k) := bk we get

1

N

Nn=1

|anbn|

1

N

Nn=1

|an|p 1

p

1

N

Nn=1

|bn|q 1

q

from Proposition 3.6.5. Multiplying by N and letting N gives ourassertion.

Proposition 3.6.8 [Minkowski inequality] Assume a probability space(, F,

P), random variables f, g :

R, and 1 p < . Then

(E

|f + g|p) 1p (E

|f|p) 1p + (E

|g|p) 1p . (3.6)

Proof. For p = 1 the inequality follows from |f + g| |f| + |g|. So assumethat 1 < p < . The convexity of x |x|p gives thata + b2

p |a|p + |b|p2and (a+b)p 2p1(ap+bp) for a, b 0. Consequently, |f+g|p (|f|+|g|)p 2p1(

|f

|p +

|g

|p) and

E|f + g|p 2p1(

E|f|p +

E|g|p).


61/71

3.6. SOME INEQUALITIES 61

Assuming now that (E

|f|p) 1p + (E

|g|p) 1p < , otherwise there is nothing toprove, we get that

E|f+g|p < as well by the above considerations. Taking

1 < q 0,

P(|f

Ef| ) E (f E f)

2

2 E f

2

2.

Proof. From Corollary 3.6.6 we get thatE

|f| < so thatE

f exists. Ap-plying Proposition 3.6.1 to

|f

E

f|2 gives that

P({|f

Ef| }) =

P({|f

Ef|2 2}) E |f E f|

2

2.

Finally, we use thatE

(fE

f)2 =E

f2 (E

f)2 E

f2.


62/71

62 CHAPTER 3. INTEGRATION


63/71

Chapter 4

Modes of convergence

4.1 Definitions

Let us introduce some basic types of convergence.

Definition 4.1.1 [Types of convergence] Let (, F, P ) be a probabil-ity space and f, f1, f2, : R be random variables.

(1) The sequence (fn)n=1 converges almost surely (a.s.) or with prob-

ability 1 to f (fn f a.s. or fn f P -a.s.) if and only ifP

(

{ : fn()

f() as n

}) = 1.

(2) The sequence (fn)n=1 converges in probability to f (fn

P f) if andonly if for all > 0 one has

P({ : |fn() f()| > }) 0 as n .

(3) If 0 < p < , then the sequence (fn)n=1 converges with respect toLp or in the Lp-mean to f (fn

Lp f) if and only ifE

|fn

f

|p

0 as n

.

For the above types of convergence the random variables have to be definedon the same probability space. There is a variant without this assumption.

Definition 4.1.2 [Convergence in distribution] Let (n, Fn, P n) and(, F,

P) be probability spaces and let fn : n R and f : R be

random variables. Then the sequence (fn)n=1 converges in distribution

to f (fnd f) if and only if

E(fn) (f) as n

for all bounded and continuous functions :R

R

.

63


64/71

64 CHAPTER 4. MODES OF CONVERGENCE

We have the following relations between the above types of convergence.

Proposition 4.1.3 Let (,F

,P

) be a probability space and f, f1, f2,

: R be random variables.

(1) If fn f a.s., then fn P f.

(2) If 0 < p < and fn Lp f, then fn P f.

(3) If fnP f, then fn d f.

(4) One has that fnd f if and only if Ffn(x) Ff(x) at each point x of

continuity of Ff(x), where Ffn and Ff are the distribution-functions of

fn and f, respectively.

(5) If fnP f, then there is a subsequence 1 n1 < n2 < n3 < such

that fnk f a.s. as k .

Proof. See [4].

Example 4.1.4 Assume ([0, 1], B([0, 1]), ) where is the Lebesgue mea-sure. We takef1 = 1I[0,1

2

), f2 = 1I[ 12

,1],

f3 = 1I[0,14 ), f4 = 1I[14 ,

12 ]

, f5 = 1I[12 ,34 )

, f6 = 1I[ 34 ,1],f7 = 1I[0,18 )

, . . .

This implies limn fn(x) 0 for all x [0, 1]. But it holds convergence inprobability fn

0: choosing 0 < < 1 we get

({x [0, 1] : |fn(x)| > }) = ({x [0, 1] : fn(x) = 0})

=

12

if n = 1, 214

if n = 3, 4, . . . , 618

if n = 7, . . ....

4.2 Some applications

We start with two fundamental examples of convergence in probability andalmost sure convergence, the weak law of large numbers and the strong lawof large numbers.

Proposition 4.2.1 [Weak law of large numbers] Let (fn)n=1 be a

sequence of independent random variables with

Ef1 = m and E (f1 m)2 = 2.


65/71

4.2. SOME APPLICATIONS 65

Thenf1 + + fn

nP m as n ,

that means, for each > 0,

limn

P

: |f1 + + fn

n m| >

0.

Proof. By Chebyshevs inequality (Corollary 3.6.9) we have that

P

:

f1 + + fn nmn

>

E |f1 + + fn nm|

2

n22

=

E(

nk=1(fk

m))

2

n22

=n2

n22 0

as n .

Using a stronger condition, we get easily more: the almost sure convergenceinstead of the convergence in probability.

Proposition 4.2.2 [Strong law of large numbers] Let (fn)n=1 be a

sequence of independent random variables withE

fk = 0, k = 1, 2, . . . , and

c := supn E f4n < . Thenf1 + + fn

n 0 a.s.

Proof. Let Sn :=n

k=1 fk. It holds

ES4n = E

n

k=1

fk

4=

E

ni,j,k,l,=1

fifjfkfl

=

nk=1

E f4k + 3

nk,l=1

k=l

E f2k E f2l ,

because for distinct {i,j,k,l} it holdsE

fif3j = E fif

2j fk = E fifjfkfl = 0

by independence. For example,E

fif3j = E fi E f

3j = 0 E f3j = 0, where one

gets that f3j is integrable by E |fj |3 ( E |fj|4)34 c 34 . Moreover, by Jensens

inequality, E

f2k2

Ef4k c.


66/71

66 CHAPTER 4. MODES OF CONVERGENCE

HenceE

f2kf2l = E f

2k E f

2l c for k = l. Consequently,

ES4n

nc + 3n(n

1)c

3cn2,

and

E

n=1

S4nn4

=n=1

E

S4nn4

n=1

3c

n2< .

This implies that S4n

n4 0 a.s. and therefore Sn

n 0 a.s.

There are several strong laws of large numbers with other, in particularweaker, conditions. Another set of results related to almost sure convergencecomes from Kolmogorovs 0-1-law. For example, we know that

n=1

1n

= but that

n=1

(1)n

nconverges. What happens, if we would choose the

signs +, randomly, for example using independent random variables n,n = 1, 2, . . . , with

P

({ : n() = 1}) = P ({ : n() = 1}) = 12

for n = 1, 2, . . . . This would correspond to the case that we choose + and according to coin-tossing with a fair coin. Put

A :=

:

n=1n()

nconverges

. (4.1)

Kolmogorovs 0-1-law will give us the surprising a-priori information that 1or 0. By other tools one can check then that in fact

P(A) = 1. To formulate

the Kolmogorov 0-1-law we need

Definition 4.2.3 [tail -algebra] Let fn : R be sequence of map-pings. Then

Fn = (fn, fn+1, . . . ) :=

f1k (B) : k = n, n + 1,...,B B(R )

and

T :=n=1

Fn .

The -algebra T is called the tail--algebra of the sequence (fn)n=1.

Proposition 4.2.4 [Kolmogorovs 0-1-law] Let (fn)n=1 be a sequence

of independent random variables. Then

P(A) {0, 1} for all A T.

Proof. See [5].


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4.2. SOME APPLICATIONS 67

Example 4.2.5 Let us come back to the set A considered in Formula (4.1).For all n {1, 2,...} we have

A =

:k=n

k()

kconverges

Fnso that A T.

We close with a fundamental example concerning the convergence in distri-bution: the Central Limit Theorem (CLT). For this we need

Definition 4.2.6 Let (, F,P

) be a probability spaces. A sequence ofIndependent random variables fn :

R

is called Identically Distributed

(i.i.d.) provided that the random variables fn have the same law, that means

P(fn ) = P (fk )

for all n, k = 1, 2,... and all R

.

Let (, F,P

) be a probability space and (fn)n=1 be a sequence of i.i.d. ran-

dom variables withE

f1 = 0 and E f21 =

2. By the law of large numbers weknow

f1 + + fnn

P

0.

Hence the law of the limit is the Dirac-measure 0. Is there a right scalingfactor c(n) such that

f1 + + fnc(n)

g,

where g is a non-degenerate random variable in the sense thatP g = 0? And

in which sense does the convergence take place? The answer is the following

Proposition 4.2.7 [Central Limit Theorem] Let (fn)n=1 be a sequence

of i.i.d. random variables withE

f1 = 0 and E f21 =

2 > 0. Then

P

f1 + + fn

n x

1

2

x

eu2

2 du

for allx R

as n , that means thatf1 + + fn

nd g

for anyg withP

(g x) =

x

e

u2

2 du.


68/71

Index

-system, 25lim infn An, 15lim infn n, 15lim supn An, 15lim supn n, 15

-system, 20-systems and uniqueness of mea-sures, 20

-Theorem, 25-algebra, 8-finite, 12

algebra, 8axiom of choice, 26

Bayes formula, 17

binomial distribution, 20Borel -algebra, 11Borel -algebra on

R

n, 20

Caratheodorys extension theorem,19

central limit theorem, 67Change of variables, 49Chebyshevs inequality, 58closed set, 11conditional probability, 16convergence almost surely, 63convergence in Lp, 63convergence in distribution, 63convergence in probability, 63convexity, 58counting measure, 13

Dirac measure, 12distribution-function, 34dominated convergence, 47

equivalence relation, 25

existence of sets, which are notBorel, 26

expectation of a random variable,41

expected value, 41

exponential distribution onR

, 22extended random variable, 52

Fubinis Theorem, 52, 54

Gaussian distribution onR

, 22geometric distribution, 21

Holders inequality, 59

i.i.d. sequence, 67independence of a family of events,

36independence of a family of ran-

dom variables, 35independence of a finite family of

random variables, 36independence of a sequence of

events, 15

Jensens inequality, 58

Kolmogorovs 0-1-law, 66

law of a random variable, 33Lebesgue integrable, 41Lebesgue measure, 21, 22Lebesgues Theorem, 47lemma of Borel-Cantelli, 17lemma of Fatou, 15lemma of Fatou for random vari-

ables, 47

measurable map, 32measurable space, 8

68


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INDEX 69

measurable step-function, 29measure, 12measure space, 12Minkowskis inequality, 60monotone class theorem, 52monotone convergence, 45

open set, 11

Poisson distribution, 21Poissons Theorem, 24probability measure, 12probability space, 12

product of probability spaces, 19random variable, 30Realization of independent random

variables, 38

step-function, 29strong law of large numbers, 65

tail -algebra, 66

uniform distribution, 21

variance, 41vector space, 51

weak law of large numbers, 64


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70 INDEX


71/71

Bibliography

[1] H. Bauer. Probability theory. Walter de Gruyter, 1996.

[2] H. Bauer. Measure and integration theory. Walter de Gruyter, 2001.

[3] P. Billingsley. Probability and Measure. Wiley, 1995.

[4] A.N. Shiryaev. Probability. Springer, 1996.

[5] D. Williams. Probability with martingales. Cambridge University Press,1991.

an introduction to probability theory - geiss

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