Application Random Process Tutorial: And Random Models in Algorithms and Intelligent Computing

Author: Gong Guanglu, Qian Minping
Publisher:
Publish Date: 2004-03-21
Features: The content of this book is the fundamental tools of stochastic modeling, suitable as a textbook or reference for senior undergraduate and graduate students in science, engineering, and management disciplines. It is also an important reference for teachers, researchers, and practitioners who use applied stochastic processes to analyze data. The book outlines the basic concepts of applied stochastic processes and their recent significant developments and important methods. It does not require readers to have knowledge of measure theory. Using non-rigorous reasoning, it follows the principle of emphasizing ideas, background, and approaches, striving to balance theory and algorithms. The book is divided into 17 chapters, including:
- A concise review and supplement of probability theory
- Random sample generation methods
- General concepts of random processes and independent increment processes
- Renewal phenomena and their theory
- Discrete-time Markov chains
- Continuous-time Markov chains
- An introduction to queuing processes
- Markov chain Monte Carlo methods
- Stochastic fields with image information and iterative Markov systems, as well as Bayesian statistical methods
- Hidden Markov models and their applications
- Gaussian second-order moment processes and time series
- Continuous-state Markov processes
- It? stochastic calculus and stochastic differential equations
- Pricing of financial securities and contingent claims
- Applications of stochastic processes in actuarial and risk modeling
- Several algorithms related to data modeling
- An introduction to discrete-state Markov control and decision processes
- An introduction to Poisson stochastic analysis and typical point processes
The content of this book is the fundamental tools of stochastic modeling, suitable as a textbook or reference for senior undergraduate and graduate students in science, engineering, and management disciplines. It is also an important reference for teachers, researchers, and practitioners who use applied stochastic processes to analyze data. In the preface, we provide some suggestions for readers who are first teaching (or reading) this book. The book is written for students, graduate students, teachers, and researchers in science, engineering, and management, so it does not require readers to have knowledge of measure theory. The book outlines the basic concepts of applied stochastic processes and their recent significant developments and important methods, covering a relatively comprehensive scope. Following the principle of emphasizing ideas, background, and approaches, it strives to balance theory and algorithms while using more non-rigorous reasoning.