ACADEMICS
Course Details

ELE609 - Probability Theory and Stochastic Processes

2022-2023 Fall term information
The course is not open this term
ELE609 - Probability Theory and Stochastic Processes
Program Theoretýcal hours Practical hours Local credit ECTS credit
MS 3 0 3 8
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving
Course objective : After introducing the basic concepts of the probability theory in the undergraduate study, in this course the theory is presented with sufficient elaboration supported with many engineering oriented examples. With this it is aimed to have the students build a solid understanding of the concepts and establish an ability to solve the problems by using these concepts as a tool.
Learning outcomes : Knows the basic components of probability model. Knows how to model the sample space in an experiment Computes the statistical properties (mean, variance, covariance, correlation) of a given one/multi variable random variable(s). Have the knowledge to follow and understand the advanced and complex probability theory related concepts. In engineering problems recognizes the random phenomena and applies the correct statistical models.
Course content : The Axioms of Probability, Probability Space Conditional probability, Bernoulli trials The Concept of a Random Variable Distribution and density functions, Conditional distributions Asymptotic approximations for binomial random variables Functions of one random variable, Transformation of a random variable Mean and Variance Concepts, Moments, Characteristic Functions Two random variables, Bivariate distributions One function of two random variables Two functions of two random variables (Jacobian matrix) Joint Moments, Joint Characteristic Functions, Conditional Bivariate Distributions Random Processes, Wide Sense and Complete Stationarity, Statistical averages and ergodicity Autocorrelation and cross-correlation functions, Gauss processes
References : Papoulis and Pillai, Probability, Random Variables, and Stochastic Processes, ; 4th Ed., Mc-Graw Hill, 2002.; Milton and Arnold, Introduction To Probability and Statistics, 4th Ed., ; Mc-Graw Hill, 2003.
Course Outline Weekly
Weeks Topics
1 The Axioms of Probability, Sample Space, Conditional Probability
2 Independence, Bernoulli Trials
3 Random Variable Concept
4 Distribution and Density Functions, Conditional Distributions
5 Asymptotic Approximations for Binomial Random Variables
6 Functions of One Random Variable, Transformation of a Random Variable
7 Mean and Variance Concepts, Moments, Characteristic Functions
8 Midterm Exam
9 Two Random Variables, Bivariate Distributions
10 One Function of Two Random Variables
11 Two Functions of Two Random Variables (Jacobian Matrix)
12 Joint Moments, Joint Characteristic Functions, Conditional Bivariate Distributions
13 Random Processes and their properties, Stationarity, Statistical averages and ergodicity
14 Autocorrelation and cross-correlation functions, Gauss processes
15 Preparation Week for Final Exams
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 0 0
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 50
Final exam 1 50
Total 100
Percentage of semester activities contributing grade success 50
Percentage of final exam contributing grade success 50
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 8 112
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 0 0 0
Quiz 0 0 0
Midterms (Study duration) 1 42 42
Final Exam (Study duration) 1 44 44
Total workload 30 97 240
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.
4. Designs and runs research projects, analyzes and interprets the results.
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.
6. Produces novel solutions for problems.
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.
8. Follows technological developments, improves him/herself , easily adapts to new conditions.
9. Is aware of ethical, social and environmental impacts of his/her work.
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering