Application of Random Processes and Random Models in Algorithms and Intelligent Computing

Author: Gong Guanglu
Publisher:
Publish Date: 2004-03-01
Features: The content of this book is the basic tools of stochastic modeling, suitable for use as a textbook or reference book for senior undergraduate and graduate students in science, engineering, and management disciplines; it is also an important reference book for teachers, researchers, and workers who use applied stochastic processes to analyze data. The book outlines the basic content of applied stochastic processes and their recent significant developments and important methods. It does not require readers to have knowledge of measure theory. Under non-strict 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, random 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, the pricing of financial securities and contingent claims, the application of stochastic processes in actuarial and risk models, 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 basic tools of stochastic modeling, suitable for use as a textbook or reference book for senior undergraduate and graduate students in science, engineering, and management disciplines; it is also an important reference book for teachers, researchers, and workers 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 content 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-strict reasoning.