HX336400 Special Lectures in Physics (Application of Bayesian Inference to Synchrotron Radiation Measurement Data)

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Science

Lecture Code HX336400 Subject Classification Specialized Education

Subject Name 物理学特別講義（ベイズ推定の放射光測定データへの応用）

Subject Name
（Katakana）ブツリガクトクベツコウギ（ベイズスイテイノホウシャコウソクテイデータヘノオウヨウ）

Subject Name in
English Special Lectures in Physics (Application of Bayesian Inference to Synchrotron Radiation Measurement Data)

Instructor To be announced.,IDETA SHINICHIRO

Instructor
(Katakana) タントウキョウインミテイ,イデタ　シンイチロウ

Campus Higashi-Hiroshima Semester/Term 4th-Year, First Semester, First Semester

Days, Periods, and Classrooms (1st) Inte

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 1.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 06 : Physics

Eligible Students

Keywords Bayesian estimation, synchrotron radiation, spectral analysis.

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 is to understand the concept of Bayesian inference and to find issues in applying Bayesian inference to your own research. The lecture will cover the following topics.

(1) Introduction to data-driven science.
(2) Review of the least squares method.
(3) Introduction to Bayesian inference, probabilistic formulation, calculation of posterior distribution, estimation of noise.
(4) Model selection.
(5) Bayesian inference summary.
(6) Bayesian spectral decomposition.
(7) Application of Bayesian inference to synchrotron radiation measurements.
(8) Exercises based on lecture content.

Class Schedule lesson1 Introduction to data science

lesson2 Concept of Bayesian estimation
lesson3 Review of the least squares method
lesson4 Introduction to Bayesian estimation (on linear regression)
lesson5 Noise and predictive distribution estimation by Bayesian estimation

lesson6 Model selection by Bayesian estimation
lesson7 Implementation of Bayesian estimation 1 (on linear regression)
lesson8 Model selection by Bayesian estimation
lesson9 Spectral decomposition using Bayesian estimation
lesson10 Implementation of Bayesian estimation 2 (on spectral decomposition)
lesson11 Application of Bayesian estimation to synchrotron radiation measurements 1 (X-ray photoelectron spectroscopy and X-ray absorption)
lesson12 Application of Bayesian estimation to synchrotron radiation measurements 2 (X-ray diffraction measurements)
lesson13 Application of Bayesian estimation to synchrotron radiation measurements 3 (Mössbauer spectroscopy)
lesson14 Application of Bayesian estimation to non-synchrotron radiation measurements 4 (crystal field estimation)
lesson15 Summary

There is no examination, but you are required to submit a report.

Text/Reference
Books,etc. Distribute documents on the day

PC or AV used in
Class,etc.

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Read the handouts/slides carefully and use your hands when following the equations to deepen your understanding. Also, as the program is distributed, deepen your understanding by performing calculations under different conditions and examining the results.

Requirements

Grading Method Evaluation is based on active participation in lectures and the level of achievement of submitted reports.

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	2025Year	School/Graduate School	School of Science
Lecture Code	HX336400	Subject Classification	Specialized Education
Subject Name	物理学特別講義（ベイズ推定の放射光測定データへの応用）
Subject Name （Katakana）	ブツリガクトクベツコウギ（ベイズスイテイノホウシャコウソクテイデータヘノオウヨウ）
Subject Name in English	Special Lectures in Physics (Application of Bayesian Inference to Synchrotron Radiation Measurement Data)
Instructor	To be announced.,IDETA SHINICHIRO
Instructor (Katakana)	タントウキョウインミテイ,イデタ　シンイチロウ
Campus	Higashi-Hiroshima	Semester/Term	4th-Year, First Semester, First Semester
Days, Periods, and Classrooms	(1st) Inte
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	1.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	06 : Physics
Eligible Students
Keywords	Bayesian estimation, synchrotron radiation, spectral analysis.
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 is to understand the concept of Bayesian inference and to find issues in applying Bayesian inference to your own research. The lecture will cover the following topics. (1) Introduction to data-driven science. (2) Review of the least squares method. (3) Introduction to Bayesian inference, probabilistic formulation, calculation of posterior distribution, estimation of noise. (4) Model selection. (5) Bayesian inference summary. (6) Bayesian spectral decomposition. (7) Application of Bayesian inference to synchrotron radiation measurements. (8) Exercises based on lecture content.
Class Schedule	lesson1 Introduction to data science lesson2 Concept of Bayesian estimation lesson3 Review of the least squares method lesson4 Introduction to Bayesian estimation (on linear regression) lesson5 Noise and predictive distribution estimation by Bayesian estimation lesson6 Model selection by Bayesian estimation lesson7 Implementation of Bayesian estimation 1 (on linear regression) lesson8 Model selection by Bayesian estimation lesson9 Spectral decomposition using Bayesian estimation lesson10 Implementation of Bayesian estimation 2 (on spectral decomposition) lesson11 Application of Bayesian estimation to synchrotron radiation measurements 1 (X-ray photoelectron spectroscopy and X-ray absorption) lesson12 Application of Bayesian estimation to synchrotron radiation measurements 2 (X-ray diffraction measurements) lesson13 Application of Bayesian estimation to synchrotron radiation measurements 3 (Mössbauer spectroscopy) lesson14 Application of Bayesian estimation to non-synchrotron radiation measurements 4 (crystal field estimation) lesson15 Summary There is no examination, but you are required to submit a report.
Text/Reference Books,etc.	Distribute documents on the day
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Read the handouts/slides carefully and use your hands when following the equations to deepen your understanding. Also, as the program is distributed, deepen your understanding by performing calculations under different conditions and examining the results.
Requirements
Grading Method	Evaluation is based on active participation in lectures and the level of achievement of submitted reports.
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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