| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA241101 |
Subject Classification |
Specialized Education |
| Subject Name |
ベイズ統計 |
Subject Name
（Katakana） |
ベイズトウケイ |
Subject Name in
English |
Bayesian Statistics |
| Instructor |
NUNES TENDEIRO JORGE |
Instructor
(Katakana) |
ナヌッシュ テンデイル ジョージ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, 1Term |
| Days, Periods, and Classrooms |
(1T) Thur9-10,Fri9-10：ENG 220 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
E
:
English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Bayes, statistics, inference |
| 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
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（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.
Intelligence 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. |
|---|
Class Objectives
/Class Outline |
To learn the basics of Bayesian statistics, using R and Stan. |
| Class Schedule |
Introduction. Bayesian inference as reallocation of credibility
Probability
Bayes' rule
Binomial proportion: Exact mathematical analysis
Markov chain Monte Carlo I: Metropolis sampling
Markov chain Monte Carlo II: Gibbs sampling, Hamiltonian Monte Carlo
Stan
Hierarchical models
One- and two-group models
Linear regression I
Linear regression II
ANOVA and ANCOVA I
ANOVA and ANCOVA II
Logistic regression
Poisson regression
This course will be evaluated using both check tests and assignments.
Students should bring their laptops to class, with a working internet connection. Installing R, RStudio, and Stan is required. |
Text/Reference
Books,etc. |
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). Academic Press. |
PC or AV used in
Class,etc. |
Handouts, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
It is best to work regularly. Follow the lecture sequence. Reproduce all examples discussed in the lectures on your own. Actively 'play' with the Shiny apps provided in-class. Work carefully through the check tests and assignments. |
| Requirements |
|
| Grading Method |
Check tests and assignments |
| 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. |