Hiroshima University Syllabus

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Japanese
Academic Year 2024Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program
Lecture Code WSN30501 Subject Classification Specialized Education
Subject Name 情報科学特別講義E
Subject Name
(Katakana)
ジョウホウカガクトクベツコウギイー
Subject Name in
English
Special Lecture on Informatics and Data Science E
Instructor See the class timetable.
Instructor
(Katakana)
ジュギョウジカンワリヲサンショウ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  Intensive
Days, Periods, and Classrooms (Int) Inte
Lesson Style Lecture Lesson Style
(More Details)
 
 
Credits 1.0 Class Hours/Week   Language of Instruction E : English
Course Level 6 : Graduate Advanced
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 01 : Mathematics/Statistics
Eligible Students
Keywords  
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)
 
Class Objectives
/Class Outline
The goal of this lecture aims at providing a brief review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, as well as variable selection in decision trees and edge selection in graphical models. The lecture will focus on methodological and computational perspective in Bayesian high dimensional modeling and applications in various statistical models, with a light touch on theoretical results. 
Class Schedule lesson1: Introduction to high dimensional statistics and Bayesian variable selection
lesson2: Spike-and-Slab priors I
lesson3: Spike-and-Slab priors II
lesson4: Continuous shrinkage priors
lesson5: Bayesian variable selection for spatial regression models
lesson6: Sparse Bayesian state-space and time-varying model
lesson7
lesson8
lesson9
lesson10
lesson11
lesson12
lesson13
lesson14
lesson15 
Text/Reference
Books,etc.
"Handbook of Bayesian Variable Selection" and reference papers within.
 
PC or AV used in
Class,etc.
 
(More Details)  
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Recommend reviewing statistical inference, linear regression model and generalized linear regression model; basic probability theory; Bayesian inference including conjugate priors, hierarchical models, MCMC algorithm, Gibbs samplers, posterior inference. 
Requirements  
Grading Method Student presentations of recent work in Bayesian variable selection or in related topics, or implement the discussed methods to new data sets. Students can optionally collaborate and propose new research project ideas which are related to lecture contents.  
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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