Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
IST 3114	INTRODUCTION TO BAYESIAN STATISTICS	ELECTIVE	3	0	0	5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ÖZLEM EGE ORUÇ

Offered to

Statistics
Statistics(Evening)

Course Objective

This course provides a basic grounding in the theory and concepts of Bayesian inference.

Learning Outcomes of the Course Unit

1	To understand the basic notions of Bayesian statistics
2	To prove and use Bayes Theorem in its various forms
3	To carry out Bayesian Inference for binomial proportion
4	To carry out Bayesian Inference for mean of normal distribution
5	To be able to carry out a test of hypothesis using a full Bayesian methodology
6	To understand the differences between classical statistics and Bayesian statistics

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	The Bayesian paradigm
2	Logic, Probability and Uncertainty
3	Bayesian Inference for Binomial Proportion (Using a Uniform Prior and Beta Prior)
4	Bayesian Inference for Binomial Proportion (Using a choosing your Prior)
5	Comparing Bayesian and Frequentist Inferences for Proportion
6	Bayesian Inference for Normal Mean with a discrete Prior
7	Bayesian Inference for Normal Mean with a continuous prior
8	Midterm Exam
9	Bayesian Inference for Normal Mean with a choosing your Normal prior
10	Bayesian Inference for difference between Means(Equal Variances)
11	Bayesian Inference for difference between Means(Unequal Variances)
12	Bayesian Inference for difference between two proportions using Normal approximation.
13	Bayesian Inference for Simple Linear Regression
14	Robust Bayesian Methods

Recomended or Required Reading

Textbook(s): W.M.Bolstad, Introduction to Bayesian Statistics , JohnWiley&Sons,2004, ISBN 0-471-27020-2(cloth)
Supplementary Book(s): J. Albert ,Bayesian Computation with R 2nd edition, Springer,2009, ISBN 978-0-387-92297-3

Planned Learning Activities and Teaching Methods

The course consists of lecture, class discussion and homework.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	ASG	ASSIGNMENT
3	FINS	FINAL EXAM
4	FCG	FINAL COURSE GRADE	MTE * 0.30 + ASG * 0.20 + FIN * 0.50
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.30 + ASG * 0.20 + RST * 0.50

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

%30(midtermexam)+%20(Homework Assignments/Presentation/Oral Exam)+%50(final exam)

Language of Instruction

English

Course Policies and Rules

Reading the related parts of the course material each week, attending the course and participating in class discussions are the requirements of the course. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at www.fef.deu.edu.tr

Contact Details for the Lecturer(s)

DEU. Faculty Sciences Department of Statistics
e-mail: ozlem.ege@deu.edu.tr
Tel: 0232 3018558

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	13	3	39
Preparations before/after weekly lectures	12	2	24
Preparation for midterm exam	1	10	10
Preparation for final exam	1	20	20
Preparing assignments	2	8	16
Final	1	2	2
Midterm	1	2	2
TOTAL WORKLOAD (hours)			113

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.3	PO.10	PO.12
LO.1	5	4	3	4
LO.2	5	4	3	4
LO.3	5	4	3	4
LO.4	5	4	3	4
LO.5	5	4	3	4
LO.6	5	4	3	4

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION