【2023新书】随机模型基础，815页pdf

随机建模是一套用于分析具有随机因素的实际系统的定量技术。这个领域高度技术化，主要由数学家发展。现有的大多数书籍都是为具有丰富数学培训的人编写的；本书将这种需求降到最低，使主题容易理解。《随机模型基础》提供了许多实际例子和应用，并弥合了基本随机过程理论与高级过程理论之间的差距。它解决了随机系统的性能评估和优化问题，并涵盖了不同的现代分析技术，如矩阵分析方法、扩散和流体极限方法。接下来，它探讨了随机模型、机器学习和人工智能之间的联系，并讨论如何利用直观方法而非传统理论方法。目标是尽量减少读者在理解本书涵盖的主题时所需的数学背景。因此，本书适合工业工程、商业与经济、计算机科学和应用数学专业的专业人士和学生。

https://www.barnesandnoble.com/w/fundamentals-of-stochastic-models-zhe-george-zhang/1142551933 1. Introduction. Part I. Fundamentals of Stochastic Models. 2. Discrete-time Markov Chains. 3. Continuous-Time Markov Chains. 4. Structured Markov Chains. 5. Renewal Processes and Embedded Markov Chains. 6. Random Walks and Brownian Motions. 7. Reflected Brownian Motion Approximations to Simple Stochastic Systems. 8. Large Queueing Systems. 9. Static Optimization in Stochastic Models. 10. Dynamic Optimization in Stochastic Models. 11. Learning in Stochastic Models. Part II. Appendices: Elements of Probability and Stochastics. A. Basics of Probability Theory. B. Conditional Expectation and Martingales. C. Some Useful Bounds, Inequalities, and Limit Laws. D. Non-linear Programming in Stochastics. E. Change of Probability Measure for a Normal Random Variable. F. Convergence of Random Variables. G. Major Theorems for Stochastic Process Limits. H. A Brief Review on