Information Package / Course Catalogue
Bayesian Data Analysis
Course Code: BİS528
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

The goal of the course is to give an introduction to the theory behind and the tools of Bayesian data analysis.

Course Content

Bayes’ theorem, base concepts of Bayesian data analysis, theory and applications of Bayesian data analysis, comparison of Bayesian and classical statistical methods.

Name of Lecturer(s)
Learning Outcomes
1.To learn the concepts of Bayes theorem
2.To be able to comprehend the philosophy of Bayesian statistical modeling
3.To be able to comprehend Bayesian models for numerous common data analysis situations, including prior elicitation
4.To be able to use software such as R, SAS or SPSS to implement Bayesian analyses
5.To be able to comprehend basic principles of both conjugate analyses and MCMC-based Bayesian analyses
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Review of Probability Concepts
Week 2 - Theoretical
Bayes' Law and the Basic Bayesian Framework
Week 3 - Theoretical
Bayesian Analyses for Basic One-Sample Models
Week 4 - Theoretical
Bayesian Linear Models
Week 5 - Theoretical
General Classes of Prior Distributions and Prior Elicitation
Week 6 - Theoretical
Some Useful Monte Carlo Methods (along with use of R)
Week 7 - Theoretical
Assessing Model Quality
Week 8 - Theoretical
Literature review and discussion (Midterm exam)
Week 9 - Theoretical
Bayesian Hypothesis Testing
Week 10 - Theoretical
Bayesian Analyses for Two- and k-Sample Models-1
Week 11 - Theoretical
Bayesian Analyses for Two- and k-Sample Models-2
Week 12 - Theoretical
Hierarchical Bayesian Models
Week 13 - Theoretical
Advanced Bayesian Models: Count Regression, Mixed Models, Models for Clustered/Longitudinal Data (Time permitting)-1
Week 14 - Theoretical
Advanced Bayesian Models: Count Regression, Mixed Models, Models for Clustered/Longitudinal Data (Time permitting)-2
Week 15 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Assignment1202
Quiz2216
Midterm Examination110212
Final Examination115217
TOTAL WORKLOAD (hours)79
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
OÇ-2
3
4
4
5
4
4
4
5
5
3
OÇ-3
3
4
3
4
3
4
3
4
3
3
OÇ-4
2
5
3
4
5
3
3
3
5
5
OÇ-5
3
4
4
4
4
3
3
4
4
2
Adnan Menderes University - Information Package / Course Catalogue
2026