| Academic Year |
2026Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA224001 |
Subject Classification |
Specialized Education |
| Subject Name |
確率過程論 |
Subject Name
（Katakana） |
カクリツカテイロン |
Subject Name in
English |
Stochastic Processes |
| Instructor |
KAWANO YU |
Instructor
(Katakana) |
カワノ ユウ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, Second Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| lecture, exercises |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
probability, stochastic process |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.
Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. |
|---|
Class Objectives
/Class Outline |
A stochastic process is a mathematical concept used to describe quantities that evolve randomly over time, such as a sequence of dice rolls or stock prices. In this course, the goal is to develop an understanding of the fundamental concepts necessary for studying stochastic processes and to acquire the basic analytical techniques used to investigate their properties. |
| Class Schedule |
lesson 1: Guidance; Basics of Probability #1
lesson 2: Basics of Probability #2
lesson 3: Stochastic Processes
lesson 4: Stochastic Difference Equations
lesson 5: Markov Chains
lesson 6: Markov Processes
lesson 7: Brownian Motion
lesson 8: Ito’s Formula
lesson 9: Stochastic Differential Equations
lesson 10: Fokker-Planck Equation
lesson 11: Stability of Stochastic Differential Equations
lesson 12: Numerical Simulation of Stochastic Differential Equations
lesson 13: Bayesian Models
lesson 14: Gaussian Process Regression and Bayesian Optimization
lesson 15: Overall Review
report, final examination |
Text/Reference
Books,etc. |
Handouts will be distributed at each lecture. |
PC or AV used in
Class,etc. |
Handouts, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Review the material so that you can understand the derivations of the theory and the computational methods. |
| Requirements |
A basic knowledge of calculus, linear algebra, and probability theory is assumed.
Please bring a PC with Python installed. |
| Grading Method |
Reports: 20%, Final exam: 80% (The final exam may be replaced with a take-home assignment in some cases.) |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
The schedule will be announced later. |
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |