KA116001 Stochastic Modeling

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA116001 Subject Classification Specialized Education

Subject Name 確率モデリング

Subject Name
（Katakana）カクリツモデリング

Subject Name in
English Stochastic Modeling

Instructor DOHI TADASHI

Instructor
(Katakana) ドヒ　タダシ

Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term

Days, Periods, and Classrooms (4T) Tues1-4：ENG 218

Lesson Style Lecture Lesson Style
(More Details) 　

Based on the fundamental theory of probability, we learn the modeling techniques applied in engineering, informatics and data science. Especially we focus on discrete-state stochastic processes and their applications.

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 Poisson process, renewal process, Markov chain, queue, reliability

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) Program of Electrical,Systems and Information Engineering
（Abilities and Skills）
・Concepts, knowledge and methods which are the basis for studies related to electrical,  systems, and information engineering.
・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.

Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Data Science Program
（Abilities and Skills）
・D2. Ability to take charge of organizational strategy and planning based on statistical evidence by making full use of a wide range of knowledge and techniques in data science.

Intelligence Science Program
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline Based on the Basic probability theory, we deal with the uncertain phenomena with time an d focus on discrete-events arising in informatics, data science and engineering fields. More specifically we lean Poisson processes, renewal processes, discrete-time Markov chain, continuous-time Markov chain, as well as the advanced theory of probability. In addition we consider typical applications such as queueing theory in computer and communication theory and reliability and maintenance engineering.

Class Schedule lesson1: Guidance, Probability space (1)
lesson2: Probability space (2)
lesson3: Random variable (1)
lesson4: Random variable (2)
lesson5: Random variable (3)
lesson6: Characterizing random variable (1)
lesson7:  Characterizing random variable (2)
lesson8:  Characterizing random variable (3)
lesson9:  Generating function and characteristic function (1)
lesson10: Generating function and characteristic function (2)
lesson11: Generating function and characteristic function (3)
lesson12: Poisson processes (1)
lesson13: Poisson processes (2)
lesson14: Renewal processes (1)
lesson15:  Renewal processes (2)

final exam (and possibly mid-term exam), plus some reports

Text/Reference
Books,etc. Probability and Stochastic Processes, Masanori Fushimi, Asakura Publishing Co.

PC or AV used in
Class,etc. 　

(More Details) Projector

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Recommended to review the course materials after the class

Requirements

Grading Method final exam (70%, possible mid-term exam), reports etc. (30%)

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

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.

Academic Year	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA116001	Subject Classification	Specialized Education
Subject Name	確率モデリング
Subject Name （Katakana）	カクリツモデリング
Subject Name in English	Stochastic Modeling
Instructor	DOHI TADASHI
Instructor (Katakana)	ドヒ　タダシ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Tues1-4：ENG 218
Lesson Style	Lecture	Lesson Style (More Details)
Based on the fundamental theory of probability, we learn the modeling techniques applied in engineering, informatics and data science. Especially we focus on discrete-state stochastic processes and their applications.
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	Poisson process, renewal process, Markov chain, queue, reliability
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)	Program of Electrical,Systems and Information Engineering （Abilities and Skills）・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering. ・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering. Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Data Science Program （Abilities and Skills）・D2. Ability to take charge of organizational strategy and planning based on statistical evidence by making full use of a wide range of knowledge and techniques in data science. Intelligence Science Program （Abilities and Skills）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	Based on the Basic probability theory, we deal with the uncertain phenomena with time an d focus on discrete-events arising in informatics, data science and engineering fields. More specifically we lean Poisson processes, renewal processes, discrete-time Markov chain, continuous-time Markov chain, as well as the advanced theory of probability. In addition we consider typical applications such as queueing theory in computer and communication theory and reliability and maintenance engineering.
Class Schedule	lesson1: Guidance, Probability space (1) lesson2: Probability space (2) lesson3: Random variable (1) lesson4: Random variable (2) lesson5: Random variable (3) lesson6: Characterizing random variable (1) lesson7: Characterizing random variable (2) lesson8: Characterizing random variable (3) lesson9: Generating function and characteristic function (1) lesson10: Generating function and characteristic function (2) lesson11: Generating function and characteristic function (3) lesson12: Poisson processes (1) lesson13: Poisson processes (2) lesson14: Renewal processes (1) lesson15: Renewal processes (2) final exam (and possibly mid-term exam), plus some reports
Text/Reference Books,etc.	Probability and Stochastic Processes, Masanori Fushimi, Asakura Publishing Co.
PC or AV used in Class,etc.
(More Details)	Projector
Learning techniques to be incorporated
Suggestions on Preparation and Review	Recommended to review the course materials after the class
Requirements
Grading Method	final exam (70%, possible mid-term exam), reports etc. (30%)
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
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.

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