Basic stochastic processes = 随机过程基础 /
副标题：无
作 者：Zdzislaw Brzezniak, Tomasz Zastawniak著.
分类号：
ISBN：9787302214861
微信扫一扫,移动浏览光盘
简介
随机过程理论在数学、科学和工程中有着越来越广泛的应用。本书包括
随机过程一些基本而又重要的内容：条件期望，Markov链，Poisson过程和
Brown运动；同时也包括Ito积分和随机微分方程等应用范围越来越广的内容
。
这是一本难得的好书，它1999年出版，到2000年已经是第3次印刷，到
2003年则重印到第6次。
本书的习题是其基本内容的延伸，而且有十分完整的解答，非常适合高
年级本科生和研究生自学使用或用作教学参考书。
目录
1. Review of Probability
1.1 Events and Probability
1.2 Random Variables
1.3 Conditional Probability and Independence
1.4 Solutions
2. Conditional Expectation
2.1 Conditioning on an Event
2.2 Conditioning on a Discrete Random Variable
2.3 Conditioning on an Arbitrary Random Variable
2.4 Conditioning on a a-Field
2.5 General Properties
2.5 Various Exercises on Conditional Expectation
2.7 Solutions
3. Martingales in Discrete Time
3.1 SequencesofRandomVariables
3.2 Filtrations
3.3 Martingales
3.4 Games or Uhance
3.5 StoppingTimes
3.5 Optional Stopping Theorem
3.7 Solutions
4. Martingale Inequalities and Convergence
4.1 Doob's Martingale Inequalities
4.2 Doob's Martingale Convergence Theorem
4.3 Uniform Integrability and L1 Convergence of Martingales
4.4 Solutions
5. Markov Chains
5.1 First Examples and Definitions
5.2 Classification of- States
5.3 Long-Time Behaviour of Markov Chains: General Case
5.4 Long-Time Behaviour of Markov Chains with Finite State Space
5.5 Solutions
6. Stochastic Processes in Continuous Time
6.1 General Notions
6.2 Poisson Process
6.2.1 Exponential Distribution and Lack of Memory
6.2.2 Construction of the Poisson Process
6.2.3 Poisson Process Starts from Scratch at Time
6.2.4 Various Exercises on the Poisson Process
6.3 Brownian Motion
6.3.1 Definition and Basic Properties
6.3.2 Increments of Brownian Motion
6.3.3 Sample Paths
6.3.4 Doob's Maximal L2 Inequality for Brownian Motion
6.3.5 Various Exercises on Brownian Motion
6.4 Solutions
7. Ito Stochastic Calculus
7 1 It6 Stochastic Integral' Definition
7.2 Examples
7.3 Properties of the Stochastic Integral
7.4 Stochastic Differential and It6 Formula
7.5 Stochastic Differential Equations
7.6 Solutions
Index
1.1 Events and Probability
1.2 Random Variables
1.3 Conditional Probability and Independence
1.4 Solutions
2. Conditional Expectation
2.1 Conditioning on an Event
2.2 Conditioning on a Discrete Random Variable
2.3 Conditioning on an Arbitrary Random Variable
2.4 Conditioning on a a-Field
2.5 General Properties
2.5 Various Exercises on Conditional Expectation
2.7 Solutions
3. Martingales in Discrete Time
3.1 SequencesofRandomVariables
3.2 Filtrations
3.3 Martingales
3.4 Games or Uhance
3.5 StoppingTimes
3.5 Optional Stopping Theorem
3.7 Solutions
4. Martingale Inequalities and Convergence
4.1 Doob's Martingale Inequalities
4.2 Doob's Martingale Convergence Theorem
4.3 Uniform Integrability and L1 Convergence of Martingales
4.4 Solutions
5. Markov Chains
5.1 First Examples and Definitions
5.2 Classification of- States
5.3 Long-Time Behaviour of Markov Chains: General Case
5.4 Long-Time Behaviour of Markov Chains with Finite State Space
5.5 Solutions
6. Stochastic Processes in Continuous Time
6.1 General Notions
6.2 Poisson Process
6.2.1 Exponential Distribution and Lack of Memory
6.2.2 Construction of the Poisson Process
6.2.3 Poisson Process Starts from Scratch at Time
6.2.4 Various Exercises on the Poisson Process
6.3 Brownian Motion
6.3.1 Definition and Basic Properties
6.3.2 Increments of Brownian Motion
6.3.3 Sample Paths
6.3.4 Doob's Maximal L2 Inequality for Brownian Motion
6.3.5 Various Exercises on Brownian Motion
6.4 Solutions
7. Ito Stochastic Calculus
7 1 It6 Stochastic Integral' Definition
7.2 Examples
7.3 Properties of the Stochastic Integral
7.4 Stochastic Differential and It6 Formula
7.5 Stochastic Differential Equations
7.6 Solutions
Index
Basic stochastic processes = 随机过程基础 /
- 名称
- 类型
- 大小
光盘服务联系方式: 020-38250260 客服QQ：4006604884
云图客服：
用户发送的提问，这种方式就需要有位在线客服来回答用户的问题，这种 就属于对话式的,问题是这种提问是否需要用户登录才能提问
Video Player
×
Audio Player
×
pdf Player
×
