GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION

SİMULATİON TECHNİQUES/5331303

COURSE INFORMATION

-- (CATALOG CONTENT)

-- (TEXTBOOK)

-- (SUPPLEMENTARY TEXTBOOK)

-- (PREREQUISITES AND CO-REQUISITES)

-- LANGUAGE OF INSTRUCTION

Turkish

-- COURSE OBJECTIVES

-- COURSE LEARNING OUTCOMES

• Ability to design and to implement a virtual experiment in the statistical programming language R to obtain the Monte Carlo estimates.

-- MODE OF DELIVERY

The mode of delivery of this course is Face to face

--WEEKLY SCHEDULE
1. Week	Introduction: Virtual and actual world.
2. Week	Scientific Modelling: Systems, random variables as inputs and outputs of a system, parameters vs statistics.
3. Week	Monte Carlo Estimation: Direct estimation of parameters via the Law of Large Numbers.
4. Week	General Methods of Generating Virtual Observations: Inverse transformation technique, acceptance-rejection technique.
5. Week	Generating Pseudo Random Numbers and Some Tests of Randomness.
6. Week	Fundamental Elements of R Programming Language.
7. Week	Midterm Exam.
8. Week	Random Sampling from Discrete Probability Distributions.
9. Week	Random Sampling from Continuous Probability Distributions.
10. Week	Discrete Event Simulation.
11. Week	Stopping Rules for a Simulation Run.
12. Week	Efficient Monte Carlo Estimators.
13. Week	Designing and Implementing Virtual Experiments in R Programming Environment.
14. Week	A Sample Monte Carlo Study on Robustness of z and t Statistics.
15. Week	Validating a Monte Carlo Study.
16. Week	Final Exam

-- TEACHING and LEARNING METHODS

-- ASSESSMENT CRITERIA

	Quantity	Total Weighting (%)
Midterm Exams	1	20
Assignment	8	10
Application	0	0
Projects	1	10
Practice	0	0
Quiz	0	0

Percent of In-term Studies		40
Percentage of Final Exam to Total Score		60

-- WORKLOAD

Activity	Total Number of Weeks	Duration (weekly hour)	Total Period Work Load
Weekly Theoretical Course Hours	14	3	42
Weekly Tutorial Hours	0	0	0
Reading Tasks	14	4	56
Searching in Internet and Library	14	1	14
Material Design and Implementation	8	3	24
Report Preparing	8	3	24
Preparing a Presentation	2	3	6
Presentation	2	1	2
Midterm Exam and Preperation for Midterm Exam	1	10	10
Final Exam and Preperation for Final Exam	1	10	10
Other (should be emphasized)			0
TOTAL WORKLOAD:			188
TOTAL WORKLOAD / 25:			7.52
Course Credit (ECTS):			7.5

-- COURSE'S CONTRIBUTION TO PROGRAM

-- NAME OF LECTURER(S)

(Doç. Dr. Meltem EKİZ , Dr. Öğr. Üyesi Osman Ufuk EKİZ)

-- WEB SITE(S) OF LECTURER(S)

(http://websitem.gazi.edu.tr/site/yavuzata; http://websitem.gazi.edu.tr/site/myata; http://istatistikseliletisim.net)

-- EMAIL(S) OF LECTURER(S)

(ufukekiz@gazi.edu.tr)

Gazi University