Hiroshima University Syllabus

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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
Subject Name in
Stochastic Modeling
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
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
(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 
Probability and Stochastic Processes, Masanori Fushimi, Asakura Publishing Co.  
PC or AV used in
(More Details) Projector 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Recommended to review the course materials after the class 
Grading Method final exam (70%, possible mid-term exam), reports etc. (30%) 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
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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