《概率論教程 》是一部講述現代概率論及其測度論應用基礎的教程，其目標讀者是該領域的研究生和相關的科研人員。內容廣泛，有許多初級教程不能涉及到得的。理論敘述嚴謹，獨立性強。有關測度的部分和概率的章節相互交織，將概率的抽像性**呈現出來。此外，還有大量的圖片、計算模擬、重要數學家的個人傳記和大量的例子。這使得表現形式*加活躍。本書由凱蘭克著。

preface
1 basic measure theory
1.1 classes of sets
1.2 set functions
1.3 the measure extension theorem
1.4 measurable maps
1.5 random variables
2 independence
2.1 independence of events
2.2 independent random variables
2.3 kolmogorov's 0-1 law
2.4 example: percolation
3 generating functions
3.1 definition and examples
3.2 poisson approximation
3.3 branching processes
4 the integral
4.1 construction and simple properties
4.2 monotone convergence and fatou's lemma
.4.3 lebesgue integral versus riemann integral
5 moments and laws of large numbers
5.1 moments
5.2 weak law of large numbers
5.3 strong law of large numbers
5.4 speed of convergence in the strong lln
5.5 the poisson process
6 convergence theorems
6.1 almost sure and measure convergence
6.2 uniform integrability
6.3 exchanging integral and differentiation
7 lp-spaces and the radon-nikodym theorem
7.1 definitions
7.2 inequalities and the fischer-riesz theorem
7.3 hilbert spaces
7.4 lebesgue's decomposition theorem
7.5 supplement: signed measures
7.6 supplement: dual spaces
8 conditional expectations
8.1 elementary conditional probabilities
8.2 conditional expectations
8.3 regular conditional distribution
9 martingales
9.1 processes, filtrations, stopping times
9.2 martingales
9.3 discrete stochastic integral
9.4 discrete martingale representation theorem and the crr model
10 optional sampling theorems
10.1 doob decomposition and square variation
10.2 optional sampling and optional stopping
10.3 uniform integrability and optional sampling
11 martingale convergence theorems and their applications
11.1 doob's inequality
11.2 martingale convergence theorems
11.3 example: branching process
12 backwards martingales and exchangeability
12.1 exchangeable families of random variables
12.2 backwards martingales
12.3 de finetti's theorem
13 convergence of measures
13.1 a topology primer
13.2 weak and vague convergence
13.3 prohorov's theorem
13.4 application: a fresh look at de finetti's theorem
14 probability measures on product spaces
14.1 product spaces
14.2 finite products and transition kernels
14.3 kolmogorov's extension theorem
14.4 markov semigroups
15 characteristic functions and the central limit theorem
15.1 separating classes of functions
15.2 characteristic functions: examples
15.3 l6vy's continuity theorem
15.4 characteristic functions and moments
15.5 the central limit theorem
15.6 multidimensional central limit theorem
16 infinitely divisible distributions
16.1 l6vy-khinchin formula
16.2 stable distributions
17 markov chains
17.1 definitions and construction
17.2 discrete markov chains: examples
17.3 discrete markov processes in continuous time
17.4 discrete markov chains: recurrence and transience
17.5 application: recurrence and transience of random walks
17.6 invariant distributions
18 convergence of markov chains
18.1 periodicity of markov chains
18.2 coupling and convergence theorem
18.3 markov chain monte carlo method
18.4 speed of convergence
19 markov chains and electrical networks
19.1 harmonic functions
19.2 reversible markov chains
19.3 finite electrical networks
19.4 recurrence and transience
19.5 network reduction
19.6 random walk in a random environment
20 ergodic theory
20.1 definitions
20.2 ergodic theorems
20.3 examples
20.4 application: recurrence of random walks
20.5 mixing
21 brownian motion
21.1 continuous versions
21.2 construction and path properties
21.3 strong markov property
21.4 supplement: feller processes
21.5 construction via l2-approximation
21.6 the space c([0, ∞))
21.7 convergence of probability measures on c([0, ∞))
21.8 donsker's theorem
21.9 pathwise convergence of branching processes
21.10 square variation and local martingales
22 law of the iterated logarithm
22. l iterated logarithm for the brownian motion
22.2 skorohod's embedding theorem
22.3 hartman-wintner theorem
23 large deviations
23.1 cramer's theorem
23.2 large deviations principle
23.3 sanov's theorem
23.4 varadhan's lemma and free energy
24 the poisson point process
24.1 random measures
24.2 properties of the poisson point process
24.3 the poisson-dirichlet distribution
25 the it6 integral
25.1 it6 integral with respect to brownian motion
25.2 it6 integral with respect to diffusions
25.3 the it6 formula
25.4 dirichlet problem and brownian motion
25.5 recurrence and transience of brownian motion
26 stochastic differential equations
26.1 strong solutions
26.2 weak solutions and the martingale problem
26.3 weak uniqueness via duality
references
notation index
name index
subject index