At the end of this course, the students; 1) Should describe the stochastical processes and do the analyses depended on probabilistic processes o the basic statistical analyses and interpret the results.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
There is no recommended optional programme component for this course.
COURSE DEFINITION
Basis of probability, discrete, continuous and random variables, expectations, conditional probability, basic statistical methods, generation functions, limit theorems, stochastic processes, Markov processes, queuing theory, Brownian motion, martingales, representation of martingales, Ito lemma, change of probability, stokastik differential equations, applications.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Stochastic processes and probability
2nd Week
Classification of stochastic processes
3rd Week
Markov Processes
4th Week
Markov Processes
5th Week
Markov Chain and Applications
6th Week
Markov Chain and Applications
7th Week
Markov Chain and Applications
8th Week
MID TERM
9th Week
Random walk models, Birth-death processes
10th Week
Random walk models, Birth
11th Week
Random walk models, Birth
12th Week
Birth-death processes
13th Week
Renewal processes
14th Week
Renewal processes
RECOMENDED OR REQUIRED READING
Introduction to Probabilistic Processes, İnal, C. Hacettepe University Publications Operations Research, Applications and Algorithms, Third Adition, Winston, W.L.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Questions/Answers,Problem Solving,Other
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
30
Assignment
5
10
Project
1
10
Total(%)
50
Contribution of In-term Studies to Overall Grade(%)
50
Contribution of Final Examination to Overall Grade(%)
50
Total(%)
100
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
