WSN30501 Special Lecture on Informatics and Data Science E

Hiroshima University Syllabus

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.

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.

Back to syllabus main page