Course Unit Code  Course Unit Title  Type of Course Unit  Year of Study  Semester  Number of ECTS Credits  İŞL23111   Elective  1  1  6 

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 
1  1. 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 multicriteria decision making methods.;  2  1 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 multicriteria decision making methods.; 

Mode of Delivery 
Daytime Class 
Prerequisites and corequisities 
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 DecisionMaking Principles
10. Selection of Risk Function and Decision Function
11. Postfinal Distribution and Expected Postpartum Loss
12. Parameter Estimation as a DecisionMaking Problem
13. Obtaining the Bayesian Estimator with Final Losses
14. Utility Function and _Statistics: Distribution Parameters 
Weekly Detailed Course Contents 

1  1. Existence of Utility Function    2  2. A Game and _Testing Statistical Hypotheses    3  3. Properties and Extension of Utility Function I    4  4. Convex Clusters    5  5. Utility Function and _Statistics: Distribution Parameters    6  6. Decision Making Under the Uncertainty of Natural Situations I    7  7. The Minimax _Principle in the Uncertainty of Nature    8  8. Bayesian Principle in the Uncertainty of Nature    9  9. Admissibility and DecisionMaking Principles    10  10. Selection of Risk Function and Decision Function    11  11. Postfinal Distribution and Expected Postpartum Loss    12  12. Parameter Estimation as a DecisionMaking Problem    13  13. Obtaining the Bayesian Estimator with Final Losses    14  14. Utility Function and _Statistics: Distribution Parameters   

Recommended or Required Reading 
1.James Berger, Statistical Decision Theory and Bayesian Analysis, SpringerVerlag, 1980.
2.Mustafa Aytaç, Necmi Gürsakal (editörler), Karar Verme, Dora Yayınları, 2015. 
Planned Learning Activities and Teaching Methods 

Assessment Methods and Criteria  
Midterm Examination  1  100  SUM  100  
Final Examination  1  100  SUM  100  Term (or Year) Learning Activities  40  End Of Term (or Year) Learning Activities  60  SUM  100 
 Language of Instruction   Work Placement(s)  none 

Workload Calculation 