Course in Probability Theory

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1 2 8 1 LAW OF LARGE NUMBERS . RANDOM SERIES*2 . Let {X„ } be independent and identically distributed with mean 0 andvariance 1 . Then we have for every x :PJ max S j > x} < 2,91'{S n > x - 2n } .1
5 .4 STRONG LAW OF LARGENUMBERS 1 1297. For arbitrary {X,, }, ifE (-f' '(IX,, I) < 00,nthen E„ X n converges absolutely a .e .8. Let {Xn }, where n = 0, ±1, ±2, . . ., be independent and identicallydistributed according to the normal distribution c (see Exercise 5 of Sec . 4 .5) .Then the series of complex-valued r .v .'s00 einxX nn=1where i = and x is real, converges a .e. and uniformly in x . (This isWiener's representation of the Brownian motion process .)*9 . Let {X n } be independent and identically distributed, taking the values0 and 2 with probability 2 each ; then00 X nn=1converges a .e. Prove that the limit has the Cantor d .f. discussed in Sec . 1 .3 .Do Exercise 11 in that section again ; it is easier now .*10 . If E n ±X n converges a .e. for all choices of ±1, where the Xn 's arearbitrary r .v .'s, then >n Xn 2 converges a.e . [HINT : Consider >n rn (t)X, (w)where the rn 's are coin-tossing r .v .'s and apply Fubini's theorem to the spaceof (t, cw) .]3n5 .4 Strong law of large numbersTo return to the strong law of large numbers, the link is furnished by thefollowing lemma on "summability" .Kronecker's lemma . Let {xk} be a sequence of real numbers, {ak} asequence of numbers > 0 and T oo . ThenPROOF .xn- < converges -n anJ=For 1 < n < oc letnnxj --* 0 .
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ACOURSE INPROBABILITYTHEORYTHIRD ED
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This book is printed on acid-free p
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vi I CONTENTS4 .3 Vague convergence
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Preface to the third editionIn this
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Preface to the second editionThis e
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Preface to the first editionA mathe
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PREFACE TO THE FIRST EDITION I xv(d
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Distribution function1 .1 Monotone
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1 .1 MONOTONE FUNCTIONS 1 3Example
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1 .1 MONOTONE FUNCTIONS 1 5havef I
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8 1 DISTRIBUTION FUNCTIONb ithe siz
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10 1 DISTRIBUTION FUNCTIONF2 - F ;
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12 ( DISTRIBUTION FUNCTIONThe next
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14 1 DISTRIBUTION FUNCTIONbecomes o
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Measure theory2 .1 Classes of setsL
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2 .1 CLASSES OF SETS 1 19and so = 6
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2.2 PROBABILITY MEASURES AND THEIR
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2 .2 PROBABILITY MEASURES AND THEIR
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2.2 PROBABILITY MEASURES AND THEIR
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3 .1 GENERAL DEFINITIONS 1 3 5of a
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3 .1 GENERAL DEFINITIONS 1 37Specif
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3 .1 GENERAL DEFINITIONS 1 39by (2)
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3 .2 PROPERTIES OF MATHEMATICAL EXP
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3.2 PROPERTIES OF MATHEMATICAL EXPE
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3 .3 INDEPENDENCE 1 533.3 Independe
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3 .3 INDEPENDENCE 1 55PROOF . We gi
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3 .3 INDEPENDENCE 1 57Corollary . I
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3 .3 INDEPENDENCE 1 59Then we haven
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3 .3 INDEPENDENCE 1 61in which ther
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3 .3 INDEPENDENCE 1 63We have thus
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3 .3 INDEPENDENCE I 65EXERCISES1 .
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3 .3 INDEPENDENCE I 67If do : {E~n
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4 .1 VARIOUS MODES OF CONVERGENCE 1
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4 .1 VARIOUS MODES OF CONVERGENCE I
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4 .1 VARIOUS MODES OF CONVERGENCE 1
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4 .2 ALMOST SURE CONVERGENCE; BOREL
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Page 99 and 100: 4 .3 VAGUE CONVERGENCE 1 85In this
Page 101 and 102: 4 .3 VAGUE CONVERGENCE 1 87PROOF .
Page 103 and 104: 4 .3 VAGUE CONVERGENCE ( 89By Sec .
Page 105 and 106: 4.4 CONTINUATION 1 918. If g„ and
Page 107 and 108: 4 .4 CONTINUATION 1 93that is linea
Page 109 and 110: 4.4 CONTINUATION 1 95A family of p
Page 111 and 112: 4 .4 CONTINUATION 1 97if X„ -+ X
Page 113 and 114: 4 .5 UNIFORM INTEGRABILITY ; CONVER
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Page 121 and 122: 5 .1 SIMPLE LIMIT THEOREMS 1 107eve
Page 123 and 124: 5 .1 SIMPLE LIMIT THEOREMS 1 109and
Page 125 and 126: 5 .1 SIMPLE LIMIT THEOREMS 1 1 1 1r
Page 127 and 128: 5.2 WEAK LAW OF LARGENUMBERS 1 1 1
Page 129 and 130: 5.2 WEAK LAW OF LARGENUMBERS 1 1 1
Page 131 and 132: 5.2 WEAK LAW OF LARGENUMBERS I 1 1
Page 133 and 134: 5 .2 WEAK LAW OF LARGENUMBERS I 1 1
Page 135 and 136: 5 .3 CONVERGENCE OF SERIES 1 12112.
Page 137 and 138: 5 .3 CONVERGENCE OF SERIES 1 123whe
Page 139 and 140: 5 .3 CONVERGENCE OF SERIES 1 1 2 5W
Page 141: 5 .3 CONVERGENCE OF SERIES 1 127Thi
Page 145 and 146: 5 .4 STRONG LAW OF LARGENUMBERS 1 1
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Page 155 and 156: 5 .5 APPLICATIONS 1 1 4 1PROOF . Su
Page 157 and 158: 5 .5 APPLICATIONS 1 1 43null set Z
Page 159 and 160: 5 .5 APPLICATIONS 1 1 45PROOF.Since
Page 161 and 162: 5 .5 APPLICATIONS 1 1 4 7One should
Page 163 and 164: 5 .5 APPLICATIONS 1 149Smile Borel,
Page 165 and 166: 6.1 GENERAL PROPERTIES; CONVOLUTION
Page 167 and 168: 6.1 GENERAL PROPERTIES ; CONVOLUTIO
Page 169 and 170: 6 .1 GENERAL PROPERTIES ; CONVOLUTI
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Page 175 and 176: x[ J6 .2 UNIQUENESS AND INVERSION 1
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Page 183 and 184: 6 .3 CONVERGENCE THEOREMS I 169For
Page 185 and 186: 6 .3 CONVERGENCE THEOREMS 1 171ther
Page 187 and 188: 6.3 CONVERGENCE THEOREMS 1 173metri
Page 189 and 190: 6 .4 SIMPLE APPLICATIONS I 175b > a
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6 .4 SIMPLE APPLICATIONS 1 179Theor
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6 .4 SIMPLE APPLICATIONS 1 181then
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6 .4 SIMPLE APPLICATIONS 1 183We en
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6 .4 SIMPLE APPLICATIONS 1 185limit
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6 .5 REPRESENTATIVE THEOREMS 1 187c
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l6 .5 REPRESENTATIVE THEOREMS 1 189
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6 .5 REPRESENTATIVE THEOREMS 1 191(
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6 .5 REPRESENTATIVE THEOREMS I 193w
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6 .5 REPRESENTATIVE THEOREMS I 195E
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6 .6 MULTIDIMENSIONAL CASE; LAPLACE
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6 .6 MULTIDIMENSIONAL CASE ; LAPLAC
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6 .6 MULTIDIMENSIONAL CASE ; LAPLAC
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6 .6 MULTIDIMENSIONAL CASE; LAPLACE
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7Central limit theorem andits ramif
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7.1 LIAPOUNOV'S THEOREM l 207are "n
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7.1 LIAPOUNOV'S THEOREM 1 209the sa
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(D7.1 LIAPOUNOV'S THEOREM 1 211If1,
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7 .1 LIAPOUNOV'S THEOREM I 213and p
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7 .2 LINDEBERG-FELLER THEOREM 1 215
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7.2 LINDEBERG-FELLER THEOREM 1 217W
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(D if Sn /s'n does7.3 RAMIFICATIONS
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7.3 RAMIFICATIONS OF THE CENTRAL LI
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7 .3 RAMIFICATIONS OF THE CENTRAL L
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7 .4 ERROR ESTIMATION 1 2 35as n -~
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22x2x27 .4 ERROR ESTIMATION 1 23 7b
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7 .4 ERROR ESTIMATION l 23 9for the
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7 .4 ERROR ESTIMATION 1 24 1PROOF .
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7.5 LAW OF THE ITERATED LOGARITHM 1
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7 .5 LAW OF THE ITERATED LOGARITHM
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7 .6 INFINITE DIVISIBILITY 1 251amo
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7.6 INFINITE DIVISIBILITY 1 253The
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7 .6 INFINITE DIVISIBILITY 1 255Sin
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7 .6 INFINITE DIVISIBILITY 1 257is
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7 .6 INFINITE DIVISIBILITY 1 259Let
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7 .6 INFINITE DIVISIBILITY 1 261ch
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8Random walk8 .1 Zero-or-one lawsIn
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8 .1 ZERO-OR-ONE LAWS 1 2 65Each S2
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8 .1 ZERO-OR-ONE LAWS 1 2 6 71 clea
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8.1 ZERO-OR-ONE LAWS 1 269Applying
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8 .2 BASIC NOTIONS 1 2 7 1have, how
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8 .2 BASIC NOTIONS 1 273Instead of
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8 .2 BASIC NOTIONS 1 275PROOF . The
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8.2 BASIC NOTIONS 1 27 7Theorem 8.2
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8.3 RECURRENCE I 2 7 9DEFINrrION .
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8.3 RECURRENCE 1 28 1by the previou
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8.3 RECURRENCE 1 2 831 A> U ( )Cm n
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8 .3 RECURRENCE 1 285< 2(1 - r) 2 +
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8 .3 RECURRENCE 1 2 8 7is a strictl
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8 .4 FINE STRUCTURE 1 289with the u
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8 .4 FINE STRUCTURE 1 29 1rearrange
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8.4 FINE STRUCTURE 1 293(D) If c,(,
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8.4 FINE STRUCTURE I 2 9 5PROOF . I
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8 .4 FINE STRUCTURE 1 2 9 7for 0 <
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8 .5 CONTINUATION 1 299(necessarily
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8 .5 CONTINUATION 1 30 1and consequ
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8 .5 CONTINUATION 1 3 0 3Lemma .For
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8 .5 CONTINUATION 1 3 0 5PROOF . Le
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8 .5 CONTINUATION 1 307implies JT(M
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8 .5 CONTINUATION 1 309The latter p
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9 .1 BASIC PROPERTIES OF CONDITIONA
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9.2 CONDITIONAL INDEPENDENCE ; MARK
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9 .2 CONDITIONAL INDEPENDENCE; MARK
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9 .2 CONDITIONAL INDEPENDENCE; MARK
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9.2 CONDITIONAL INDEPENDENCE ; MARK
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9 .2 CONDITIONAL INDEPENDENCE ; MAR
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9 .2 CONDITIONAL INDEPENDENCE ; MAR
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9 .3 BASIC PROPERTIES OF SMARTINGAL
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9.3 BASIC PROPERTIES OF SMARTINGALE
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9.3 BASIC PROPERTIES OF SMARTINGALE
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9 .4 INEQUALITIES AND CONVERGENCE 1
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9.5 APPLICATIONS 1 361PROOF. Let A
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9 .5 APPLICATIONS 1 363extension of
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9.5 APPLICATIONS 1 365(V)The strong
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9.5 APPLICATIONS 1 3 6 7But it is t
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9 .5 APPLICATIONS 1 369We have ther
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9 .5 APPLICATIONS ( 3712. Suppose t
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9 .5 APPLICATIONS 1 373is due to Mc
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Supplement : Measure andIntegralFor
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I CONSTRUCTION OF MEASURE 1 377The
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1 CONSTRUCTION OF MEASURE 1 379It f
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2 CHARACTERIZATION OF EXTENSIONS 1
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3 MEASURES IN R 1 387Now we use Def
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3 MEASURES IN R 1 389The right-cont
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3 MEASURES IN R 1 39 1Therefore the
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3 MEASURES IN R 1 393If we intersec
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4 INTEGRAL 1 395has cardinal 2C whi
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4 INTEGRAL 1 397The order of summat
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n4 INTEGRAL 1 399If W E A k , then
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4 INTEGRAL 1 401Theorem 9 . Let If,
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4 INTEGRAL I 403inThe three theorem
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4 INTEGRAL 1 405To prove this we ma
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5 APPLICATIONS 1 407Curiously, the
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5 APPLICATIONS 1 409When Uk is gene
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log ~ _ .d~ f~ - dx = log ,5 APPLIC
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General bibliography[The five divis
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IndexAbelian theorem, 292Absolutely
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INDEX 1 417GGambler's ruin, 343Gamb
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INDEX 1 419improper, infinite, 410p
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Course in Probability Theory

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