国外数学名著系列:现代概率论基础(影印版)(第2版)

目 录内容简介
Contents
Preface to the Second Edition v
Preface to the First Edition vii
1. Measure Theory-Basic Notions 1
Measurable sets and functions
measures and integration
monotone and dominated convergence
transformation of integrals
product measures and Fubinis theorem
LP-spaces and projection
approximation
measure spaces and kernels
2. Measure Theory——Key Results 23
Outer measures and extension
Lebesgue and Lebesgue-Stieltjes measures
Jordan-llahn and Lebesgue decompositions
Radon-Nikodym theorem
Lebesgues differentiation theorem
functions of jinite varvation
Riesz representation theorem
Haar and invariant measures
3. Processes,Distributions,and Independence 45
Random elements and processes
distributions and erpectatiorz
independence
zero-one laws
Borel-Cantelli lemma
Bernoulli seqzrences and existence
moments and continuity of paths
4. Random Sequences,Series,and Averages 62
Convergence in probability and in Lp
uniform integrability and tightness
convergence in distribution
convergence of random series
strong laws of large numbers
Portmanteau theorem
continuous mapping and approximation
coupling and measurability
5. Characteristic Functions and Classical Limit Theorems 83
Uniqueness afnd continuity theoretm
Poisson convergence
positive and symmetric terms
Lindebergs condition
general Gaussiatn conuergence
weak laws of large numbers
docmain of Gaussian attraction
vague arnd weak compactness
6. Conditioning and Disintegration 103
Conditiotnal expectations and probabilities
regular conditiotnal distributions
disintegration
conditional independence
transfer and coupling
existence of sequences and processes
extension through conditioning
7. Martingales and Optional Times 119
Filtrrations atnd optional times
random time-change
martingale property
optional stopping and sampling
tmaxitmum and upcrossing inequalities
martingale convergence regularity and closure
limits of conditional expectations
regularization of submartingales
8. Markov Processes and Discrete-Time Chains 140
Matrkov property and transition kernels
finite-dimensional distributions atnd existence
space and time homogeneity
strong Mafrkou property arnd excursions
invariant distributions and stationarity
recurrence and transience
ergodic behavior of irreducible chains
mean recurrence times
9. Random Walks and Renewal Theory 159
Recurrecnce and transience
dependence on dimensiotn
general recurrence criteria
symmetry and duality
Wiener-Hopf factorization
ladder time and height distn:bution
stationary renewal process
renewal theorem
10- Stationary Processes and Ergodic Theory 178
Stationanty invariance and ergodicity
discrete-aud continuous-time ergodic theorems
moment and maximum inequalities
multivariate ergodic theorems
sample intensity of a random measure
subadditivity and products of random matrices
conditioning ancl ergodic decomposition
shzlt coupling and the invariant a-field
11. Special Notions of Symmetry and Invariance 202
Palm distn:butions atnd inversiorn formulas
stationarity and cycle stationarity
local hitting and conditioning
ergodic properties of Palm measures
exchangeable sequences and processes
strong stationarity and predictable sampling
ballot theorems
entropy and information
12. Poisson and Pure Jump-Type Markov Processes 224
Random measures and point processes
Cox processes randomization and thinning
mixed Poisson and binomial processes
independence and symmetry critert:a
Markov transition and rate kernels
embedded Markov chains and erplosion
compound and pseudo-Poisson processes
ergodic behavior of irreducible chains
13. Gaussian Processes and Brownian Motion 249
Symmetries of Gaussian distributiotn
existence and path properties of Btrownian motion
strong Markou and reflection properties
arcsine atnd uniform laws
law of the iterated logarithm
Wiener integrals and isonormal Gaussian processes
multiple Wiener-Ito integrals
chaos erpansion of Brownian functionals
14. Skorohod Embedding and Invariance Principles 270
Embedding of random variables
approximation of random walks
functional central limit theorerm
laws of the iterated logarithm
arcsine laws
approximation of rrefnewal processes
empirical distribution jurtctions
embedding afnd approximation of martingales
15. Independent Increments and Infinite Divisibility 285
Regularity and integral representation
Levy proces8es and subordinators
stable processes and Jirst-passage times
infinitely divisible distributions
chatracterristics and convergence criteria
approximation of Levy processes and ratndom walks
limit theorems for null arrays
convergence of extremes
16. Convergence of Random Pr
Preface to the Second Edition v
Preface to the First Edition vii
1. Measure Theory-Basic Notions 1
Measurable sets and functions
measures and integration
monotone and dominated convergence
transformation of integrals
product measures and Fubinis theorem
LP-spaces and projection
approximation
measure spaces and kernels
2. Measure Theory——Key Results 23
Outer measures and extension
Lebesgue and Lebesgue-Stieltjes measures
Jordan-llahn and Lebesgue decompositions
Radon-Nikodym theorem
Lebesgues differentiation theorem
functions of jinite varvation
Riesz representation theorem
Haar and invariant measures
3. Processes,Distributions,and Independence 45
Random elements and processes
distributions and erpectatiorz
independence
zero-one laws
Borel-Cantelli lemma
Bernoulli seqzrences and existence
moments and continuity of paths
4. Random Sequences,Series,and Averages 62
Convergence in probability and in Lp
uniform integrability and tightness
convergence in distribution
convergence of random series
strong laws of large numbers
Portmanteau theorem
continuous mapping and approximation
coupling and measurability
5. Characteristic Functions and Classical Limit Theorems 83
Uniqueness afnd continuity theoretm
Poisson convergence
positive and symmetric terms
Lindebergs condition
general Gaussiatn conuergence
weak laws of large numbers
docmain of Gaussian attraction
vague arnd weak compactness
6. Conditioning and Disintegration 103
Conditiotnal expectations and probabilities
regular conditiotnal distributions
disintegration
conditional independence
transfer and coupling
existence of sequences and processes
extension through conditioning
7. Martingales and Optional Times 119
Filtrrations atnd optional times
random time-change
martingale property
optional stopping and sampling
tmaxitmum and upcrossing inequalities
martingale convergence regularity and closure
limits of conditional expectations
regularization of submartingales
8. Markov Processes and Discrete-Time Chains 140
Matrkov property and transition kernels
finite-dimensional distributions atnd existence
space and time homogeneity
strong Mafrkou property arnd excursions
invariant distributions and stationarity
recurrence and transience
ergodic behavior of irreducible chains
mean recurrence times
9. Random Walks and Renewal Theory 159
Recurrecnce and transience
dependence on dimensiotn
general recurrence criteria
symmetry and duality
Wiener-Hopf factorization
ladder time and height distn:bution
stationary renewal process
renewal theorem
10- Stationary Processes and Ergodic Theory 178
Stationanty invariance and ergodicity
discrete-aud continuous-time ergodic theorems
moment and maximum inequalities
multivariate ergodic theorems
sample intensity of a random measure
subadditivity and products of random matrices
conditioning ancl ergodic decomposition
shzlt coupling and the invariant a-field
11. Special Notions of Symmetry and Invariance 202
Palm distn:butions atnd inversiorn formulas
stationarity and cycle stationarity
local hitting and conditioning
ergodic properties of Palm measures
exchangeable sequences and processes
strong stationarity and predictable sampling
ballot theorems
entropy and information
12. Poisson and Pure Jump-Type Markov Processes 224
Random measures and point processes
Cox processes randomization and thinning
mixed Poisson and binomial processes
independence and symmetry critert:a
Markov transition and rate kernels
embedded Markov chains and erplosion
compound and pseudo-Poisson processes
ergodic behavior of irreducible chains
13. Gaussian Processes and Brownian Motion 249
Symmetries of Gaussian distributiotn
existence and path properties of Btrownian motion
strong Markou and reflection properties
arcsine atnd uniform laws
law of the iterated logarithm
Wiener integrals and isonormal Gaussian processes
multiple Wiener-Ito integrals
chaos erpansion of Brownian functionals
14. Skorohod Embedding and Invariance Principles 270
Embedding of random variables
approximation of random walks
functional central limit theorerm
laws of the iterated logarithm
arcsine laws
approximation of rrefnewal processes
empirical distribution jurtctions
embedding afnd approximation of martingales
15. Independent Increments and Infinite Divisibility 285
Regularity and integral representation
Levy proces8es and subordinators
stable processes and Jirst-passage times
infinitely divisible distributions
chatracterristics and convergence criteria
approximation of Levy processes and ratndom walks
limit theorems for null arrays
convergence of extremes
16. Convergence of Random Pr
目 录内容简介
要使我国的数学事业更好地发展起来,需要数学家淡泊名利并付出更艰苦地努力。另一方面,我们也要从客观上为数学家创造更有发展数学事业的外部环境,这主要是加强对数学事业的支持与投资力度,使数学家有较好的工作与生活条件,其中了包括改善与加强数学的出版工作。
修订版增加了四章,并对原版内容作了大量修改。
修订版增加了四章,并对原版内容作了大量修改。
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