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.
Lecturer
Date
14th October, 2024 ~ 6th January, 2025
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Monday,Wednesday | 16:10 - 17:50 | A3-1-103 | ZOOM 03 | 242 742 6089 | BIMSA |
Website
Prerequisite
Probability Theory/Statistics, Linear Algebra, Real Analysis/Functional Analysis, Ordinary Differential Equation
Reference
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
Audience
Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
Yes
Notes Public
Yes
Language
Chinese
, English
Lecturer Intro
Congwei Song received the master degree in applied mathematics from the Institute of Science in Zhejiang University of Technology, and the Ph.D. degree in basic mathematics from the Department of Mathematics, Zhejiang University, worked in Zhijiang College of Zhejiang University of Technology as an assistant from 2014 to 2021, from 2021 on, worked in BIMSA as asistant researcher. His research interests include machine learning, as well as wavelet analysis and harmonic analysis.