Course in Probability Theory

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1 CONDITIONING346. MARKOV PROPERTY . MARTINGALEfi at n . This example shows why in optional sampling the option may be takeneven with the knowledge of the present moment under certain conditions . Inthe case here the present (namely fi A n) may leave one no choice!14 . In the gambler's ruin problem, suppose that S1 has the distributionP81 + (1 - P)8-1, P 0 iand let d = 2 p - 1 . Show that (_F(Sy) = d e (y) . Compute the probabilities ofruin by using difference equations to deduce (9(y), and vice versa .15 . Prove that for any L 1-bounded smartingale {X,, ~i n , n E N,,, }, andany optional a, we have c (jXaI) < oo . [iurcr : Prove the result first for amartingale, then use Doob's decomposition .]*16 . Let {Xn, n } be a martingale : x1 = X1, xn = Xn - X, -I for n > 2 ;let vn E :Tn_1 for n > 1 where A = 9,Tj ; now putnj=1vjxj .Show that IT,, 0~,7} is a martingale provided that Tn is integrable for every n .The martingale may be replaced by a smartingale if v, > 0 for every n . Asa particular case take vn = l{n
9 .4 INEQUALITIES AND CONVERGENCE 1 347since it takes only a finite number of values, Theorem 9 .3.4 shows that thepair {X(,X n } forms a submartingale . If we writeM = { max Xj1 A on M, hence the first inequality follows)'J (M) < [ xadA < f X n d A;M Mthe second is just a cruder consequence .Similarly let fi be the first j such that X j < -A if there is such a j inN, otherwise let ,B = n . Put alsoMk = { min X j < -A) .1
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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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4.2 ALMOST SURE CONVERGENCE; BOREL-
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4 .2 ALMOST SURE CONVERGENCE ; BORE
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4 .3 VAGUE CONVERGENCE 1 85In this
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4 .3 VAGUE CONVERGENCE 1 87PROOF .
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4 .3 VAGUE CONVERGENCE ( 89By Sec .
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4.4 CONTINUATION 1 918. If g„ and
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4 .4 CONTINUATION 1 93that is linea
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4.4 CONTINUATION 1 95A family of p
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4 .4 CONTINUATION 1 97if X„ -+ X
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4 .5 UNIFORM INTEGRABILITY ; CONVER
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4.5 UNIFORM INTEGRABILITY ; CONVERG
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5 .1 SIMPLE LIMIT THEOREMS 1 107eve
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5 .1 SIMPLE LIMIT THEOREMS 1 109and
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5 .1 SIMPLE LIMIT THEOREMS 1 1 1 1r
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5.2 WEAK LAW OF LARGENUMBERS 1 1 1
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5.2 WEAK LAW OF LARGENUMBERS 1 1 1
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5.2 WEAK LAW OF LARGENUMBERS I 1 1
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5 .2 WEAK LAW OF LARGENUMBERS I 1 1
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5 .3 CONVERGENCE OF SERIES 1 12112.
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5 .3 CONVERGENCE OF SERIES 1 123whe
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5 .3 CONVERGENCE OF SERIES 1 1 2 5W
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5 .3 CONVERGENCE OF SERIES 1 127Thi
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5 .4 STRONG LAW OF LARGENUMBERS 1 1
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5.4 STRONG LAW OF LARGENUMBERS 1 13
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5.4 STRONG LAW OF LARGENUMBERS 1 13
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is an r cv) as follows co) (o) is c
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5 .5 APPLICATIONS 1 1 4 1PROOF . Su
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5 .5 APPLICATIONS 1 1 43null set Z
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5 .5 APPLICATIONS 1 1 45PROOF.Since
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5 .5 APPLICATIONS 1 1 4 7One should
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5 .5 APPLICATIONS 1 149Smile Borel,
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6.1 GENERAL PROPERTIES; CONVOLUTION
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6.1 GENERAL PROPERTIES ; CONVOLUTIO
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6 .1 GENERAL PROPERTIES ; CONVOLUTI
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CS6 .1 GENERAL PROPERTIES; CONVOLUT
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6 .1 GENERAL PROPERTIES ; CONVOLUTI
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x[ J6 .2 UNIQUENESS AND INVERSION 1
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6 .2 UNIQUENESS AND INVERSION 1 163
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6 .2 UNIQUENESS AND INVERSION 1 165
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I6 .2 UNIQUENESS AND INVERSION 1 1
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6 .3 CONVERGENCE THEOREMS I 169For
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6 .3 CONVERGENCE THEOREMS 1 171ther
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6.3 CONVERGENCE THEOREMS 1 173metri
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6 .4 SIMPLE APPLICATIONS I 175b > a
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6 .4 SIMPLE APPLICATIONS I 177k-1 j
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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,(,
Page 309 and 310: 8.4 FINE STRUCTURE I 2 9 5PROOF . I
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Page 313 and 314: 8 .5 CONTINUATION 1 299(necessarily
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Page 341 and 342: 9 .2 CONDITIONAL INDEPENDENCE; MARK
Page 343 and 344: 9.2 CONDITIONAL INDEPENDENCE ; MARK
Page 345 and 346: 9 .2 CONDITIONAL INDEPENDENCE ; MAR
Page 347 and 348: 9 .2 CONDITIONAL INDEPENDENCE ; MAR
Page 349 and 350: 9 .3 BASIC PROPERTIES OF SMARTINGAL
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Page 363 and 364: 9 .4 INEQUALITIES AND CONVERGENCE 1
Page 375 and 376: 9.5 APPLICATIONS 1 361PROOF. Let A
Page 377 and 378: 9 .5 APPLICATIONS 1 363extension of
Page 379 and 380: 9.5 APPLICATIONS 1 365(V)The strong
Page 381 and 382: 9.5 APPLICATIONS 1 3 6 7But it is t
Page 383 and 384: 9 .5 APPLICATIONS 1 369We have ther
Page 385 and 386: 9 .5 APPLICATIONS ( 3712. Suppose t
Page 387 and 388: 9 .5 APPLICATIONS 1 373is due to Mc
Page 389 and 390: Supplement : Measure andIntegralFor
Page 391 and 392: I CONSTRUCTION OF MEASURE 1 377The
Page 393 and 394: 1 CONSTRUCTION OF MEASURE 1 379It f
Page 395 and 396: 2 CHARACTERIZATION OF EXTENSIONS 1
Page 401 and 402: 3 MEASURES IN R 1 387Now we use Def
Page 403 and 404: 3 MEASURES IN R 1 389The right-cont
Page 405 and 406: 3 MEASURES IN R 1 39 1Therefore the
Page 407 and 408: 3 MEASURES IN R 1 393If we intersec
Page 409 and 410: 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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