Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İŞL-23-111Elective126
Level of Course Unit
Second Cycle
Objectives of the Course
To provide information about different models and decision analysis techniques for making decisions under certainty and uncertainty by using concepts such as statistical decision theory, utility theory, decision trees, Bayes theorem.
Name of Lecturer(s)
Dr. Öğr. Üyesi Polad ALİYEV
Learning Outcomes
11. To know the basic concepts of statistical decision theory.; 2 . To be able to define the decision making problem.; 3. To be able to solve cost structured decision problems; 4. To know the concepts and rules of game theory.; 5. To be able to apply the analysis of decision making under uncertainty and risk. 6 . To be able to apply decision tree analysis; 7. To be able to use sample information while making statistical decisions; 8 . Using Bayes' theorem while making statistical decisions; 9 . To be able to apply Markov analysis; 10. To know the multi-criteria decision making methods.;
21 To know the basic concepts of statistical decision theory.; 2 To be able to define the decision making problem.; 3 To be able to solve cost structured decision problems; 4 To know the concepts and rules of game theory.; 5 To be able to apply the analysis of decision making under uncertainty and risk. 6 To be able to apply decision tree analysis; 7 To be able to use sample information while making statistical decisions; 8 Using Bayes' theorem while making statistical decisions; 9 To be able to apply Markov analysis; 10 To know the multi-criteria decision making methods.;
Mode of Delivery
Daytime Class
Prerequisites and co-requisities
none
Recommended Optional Programme Components
none
Course Contents
1 Existence of Utility Function 2. A Game and _Testing Statistical Hypotheses 3. : Properties and Expansion of Utility Function II 4. Convex Clusters I 5. Utility Function and _Statistics: Distribution Parameters 6. Decision Making Under the Uncertainty of Natural Situations I 7. The Minimax _Principle in the Uncertainty of Nature 8. Bayesian Principle in the Uncertainty of Nature 9. Admissibility and Decision-Making Principles 10. Selection of Risk Function and Decision Function 11. Postfinal Distribution and Expected Postpartum Loss 12. Parameter Estimation as a Decision-Making Problem 13. Obtaining the Bayesian Estimator with Final Losses 14. Utility Function and _Statistics: Distribution Parameters
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
11. Existence of Utility Function
22. A Game and _Testing Statistical Hypotheses
33. Properties and Extension of Utility Function I
44. Convex Clusters
55. Utility Function and _Statistics: Distribution Parameters
66. Decision Making Under the Uncertainty of Natural Situations I
77. The Minimax _Principle in the Uncertainty of Nature
88. Bayesian Principle in the Uncertainty of Nature
99. Admissibility and Decision-Making Principles
1010. Selection of Risk Function and Decision Function
1111. Postfinal Distribution and Expected Postpartum Loss
1212. Parameter Estimation as a Decision-Making Problem
1313. Obtaining the Bayesian Estimator with Final Losses
1414. Utility Function and _Statistics: Distribution Parameters
Recommended or Required Reading
1.James Berger, Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, 1980. 2.Mustafa Aytaç, Necmi Gürsakal (editörler), Karar Verme, Dora Yayınları, 2015.
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
Work Placement(s)
none
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination111
Final Examination111
Problem Solving6212
Question-Answer6212
Criticising Paper5420
Individual Study for Mid term Examination7642
Individual Study for Final Examination10880
TOTAL WORKLOAD (hours)168
Contribution of Learning Outcomes to Programme Outcomes
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1
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13
LO13434334343344
LO23344433333344
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
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