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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 4010 STOCHASTIC PROCESSES COMPULSORY 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR KADIR ERTAŞ

Offered to

Econometrics (Evening) Econometrics

Course Objective

The purpose of this course is to show that probability theory is applicable to management sciences, engineeering, marketing, computer sciences and the other social sciences.

Learning Outcomes of the Course Unit

1   To be able to understand the stochastic processes and the transformation of stochastics processes 2   To be able to show that probabilistic models are applicable to many real life problems 3   To be able to understand how to form the stochastic model under basic assumptions 4   To be able to handle derivative and partial derivative equations of stochastic models 5   To be able to contain under the mathematical form of the probability model. some suitable solution of equations in partial derivatives as long as the initial conditions

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction, Events, probability concept; Understanding the concept of stochastic processes 2 The expected value of random variables, well-known discrete and continues distributions usefull for stochastic processes. 3 Concepts of moment generating function, factorial moment generating function 4 Approximation of binomial distribution to the poisson distribution under certain conditions. Proof of this concept using moment generating function and probability generating function. 5 Approximate Poisson Process: Definition and solution of Approximate Poisson Process 6 Linear markovian birth process: Definition and solution of Linear markovian birth process 7 Probability generating function for solving the Linear markovian birth process. Moment generating function for solving the Linear markovian birth process. 8 Mid-term 9 Mid-term 10 Linear markovian death process: Definition and solution of Linear markovian death process 11 Probability generating function for solving the Linear markovian death process. Moment generating function for solving the Linear markovian death process. 12 Linear markovian birth and death process: Definition and solution of Linear markovian birth and death process 13 Probability generating function for solving the Linear markovian birth and death process. Moment generating function for solving the Linear markovian birth and death process. 14 Obtaining the linear differantial equation of the Linear markovian birth and death process.

Recomended or Required Reading

1-Introduction to Probability Models Sheldon, M. ROSS; Academic Press. 7th edition 2-Simulation, Modelling and Analysis; Law, A. And Kelton, W. McGraw-Hill, 2nd Edition, New York. 3-Matrix Operations, Richard Bronson, Schaum Outline Series, McGraw-Hill, 1989.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Doç. Dr. Kadir ERTAŞ E-mail: kadir.ertas@deu.edu.tr Room: 634 Tel: 0 (232) 301 03 66 Int : 10 366

Office Hours

Thursday: 16:00 - 18:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 2 24 Preparation for midterm exam 1 25 25 Preparation for final exam 1 28 28 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 115

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1