Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
2EEE586PROBABILITY AND STOCHASTIC PROCESS3+0+036

Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND ELECTRONICS ENGINEERING
Mode of Delivery Face to Face
Type of Course Unit Elective
Objectives of the Course The purpose of this course is to analyze and develop the probability models used in engineering problems
Course Content Review of probability theory and random variables. Sequence of random variables, convergence concepts. Stochastic processes: Correlation and power spectra, stationarity, linear systems with random inputs, second order processes; stochastic continuity differentation and integration in quadratic mean; Gaussian processes; Poisson processes, shot noise; Markov processes; orthogonal expansions, least mean square error estimation.
Course Methods and Techniques Reading, Problem Solving, Questioning, Discussion
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Taner İnce
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Schaums Probability, Random Variables And Random Processes, Second Edition, Hwei P. Hsu.
Introduction to Probability, Second Edition, Dimitri P. Bertsekas ve John N. Tsitsiklis.
PROBABILITY AND RANDOM PROCESSES FOR ELECTRICAL AND COMPUTER ENGINEERS, JOHN A. GUBNER

Course Category
Mathematics and Basic Sciences %60
Engineering %40

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 7 98
Assignments 7 4 28
Mid-terms 1 3 3
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 6 174

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Knows the basic components of probability model.
2 Knows how to model the sample space in an experiment
3 Computes the statistical properties (mean, variance, covariance, correlation) of a given one/multi variable random variable(s).
4 Have the knowledge to follow and understand the advanced and complex probability theory related concepts.
5 In engineering problems recognizes the random phenomena and applies the correct statistical models.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to probability
2 Discrete random variables
3 Continuous random variables
4 Cumulative distribution functions and their applications
5 Bivariate random variables
6 Covariance and Correlation Coefficient
7 N-Variate Random Variables
8 Functions of Random Variables
9 Characteristic Functions
10 Random Processes
11 Advanced concepts in random processes
12 Analysis and Processing of Random Processes
13 Response of Linear Systems to Random Inputs
14 Fourier Transform of Random Processes


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4
All 4 4 4
C1 5 4 4
C2 5 4 4
C3 5 4 4
C4 5 4 4
C5 5 4 4

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https://obs.gantep.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=147983&lang=en