Stochastic Process
**Course Introduction: Stochastic Processes**
This course delves into the academic discipline of Stochastic Processes, focusing on both theoretical analysis and practical applications. Students will explore foundational concepts such as Markov chains and Poisson processes, gaining a deep understanding of probabilistic modeling. The course emphasizes a hands-on approach, incorporating programming demonstrations to illustrate key ideas and enhance comprehension. Through practical exercises, students will apply stochastic methodologies to solve real-world problems in diverse fields like finance and engineering. This blend of theory, application, and programming ensures a comprehensive learning experience that prepares students for advanced study and professional challenges in stochastic modeling.
This course delves into the academic discipline of Stochastic Processes, focusing on both theoretical analysis and practical applications. Students will explore foundational concepts such as Markov chains and Poisson processes, gaining a deep understanding of probabilistic modeling. The course emphasizes a hands-on approach, incorporating programming demonstrations to illustrate key ideas and enhance comprehension. Through practical exercises, students will apply stochastic methodologies to solve real-world problems in diverse fields like finance and engineering. This blend of theory, application, and programming ensures a comprehensive learning experience that prepares students for advanced study and professional challenges in stochastic modeling.
讲师
日期
2024年10月14日 至 2025年01月06日
位置
Weekday | Time | Venue | Online | ID | Password |
---|---|---|---|---|---|
周一,周三 | 16:10 - 17:50 | A3-1-103 | ZOOM 03 | 242 742 6089 | BIMSA |
网站
修课要求
Probability Theory/Statistics, Linear Algebra, Real Analysis/Functional Analysis, Ordinary Differential Equation
参考资料
1. Bernt Øksendal. Stochastic differential equations. In Stochastic differential equations, pp. 65–84. Springer, 2003.
2. https://en.wikipedia.org/wiki/Stochastic_process
2. https://en.wikipedia.org/wiki/Stochastic_process
听众
Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
公开
笔记公开
公开
语言
中文
, 英文
讲师介绍
宋丛威于2011年在浙江大学理学院取得应用数学硕士学位,于2014年在浙江大学数学系取得基础数学博士学位,2014-2021年在浙江工业大学之江学院任讲师,2021至今任BIMSA助理研究员。主要研究方向:小波分析,调和分析,机器学习。