This page has been produced for providing students with general informations and guidelines on the course of Advanced Probability and Stochastic Processes. If you have any questions after reading this page, send a mail to your professor.
You can download the following information written in PDF format. Click SYLLABUS, print, and bring the hardcopy to the first class hour!!!
COURSE NAME
l Advanced Probability and Random Processes (Graduate ECE5910 Class)
LECTURER
l Professor Joong Kyu Kim(Rm#: 21225, Tel: 0312907122)
COURSE OBJECTIVE
l To Learn the basics on probability, random variables, and stochastic processes in order to apply the concepts to a wide range of electrical, and electronis engineering fields.
COURSE DESCRIPTION
l Basic concepts of probability theory. Random variables: discrete, continuous, and conditional probability distributions; averages and independence. Introduction to discrete and continuous random processes: wide sense stationarity, correlation and spectral density.
PREREQUISITE
l Probability and Statistics
l Probability and Random Processes
TEXTBOOK
l Probability, Random Variables, and Random Signal Principles by P.Z.Peebles Jr.
REFERENCE
l Elements of Engineering Probability & Statisticsby R.E.Ziemer
l Probability and Random Processes by W.B.Davenport Jr.
l Probability, Random Variables, and Stochastic Processes by A.Papoulis
l Probability and Random Processes by A.LeonGarcia
l Probability and Stochastic Processes by Yates and Goodman
CLASSNOTE
l For your convenience, the classnote in PS and PDF forms will be distributed in advance!!!
GRADE POLICY
Midterm Exam 
40% 
Final Exam 
50% 
Attendance 
10% 
Total 
100% 
Note:
l All the exams are closed books, but you are allowed to bring one page of A4 size handwritten reference sheet to each examination
l Homeworks will not be assigned during the course of the semester, but you are strongly encouraged to solve some of the problem sets in each chapter of the textbook as well as the references.
l No grade change will be permitted at the end of the semester. (e.g. C or D to F)
TOPICS TO COVER
l Probability
l The Random Variable
l Operations on One Random Variable  Expectation
l Multiple Random Variables
l Operations on Multiple Random Variables
l Random Processes  Temporal Characteristics
l Random Processes  Spectral Characteristic
l Linear Systems with Random Inputs
l Optimum Linear Systems
l Some Practical Applications of the Theory
WEEKLY SCHEDULE
Week No. 
Detailed Topics 
Week#1 
Concept of probability: probability space, review of set theory, probability axioms 
Week#2 
Conditional probability, total probability law, Bayes Theorem, independent events, theory of counting 
Week#3 
Order space, Bernoulli trials, concepts of random variables, and probability distribution function 
Week#4 
Continuity axiom, classification of random variables, probability density function 
Week#5 
Gaussian and uniform random variables, conditional distribution and density functions, mathematical expectation 
Week#6 
Characteristic function, moment generating function, nonlinear function of random variables 
Week#7 
Extension of above concepts to two random variable cases, statistical independence, correlation 
 Midterm Examination  

Week#8 
Function of multiple random variables, introduction to estimation theory: LMSE(Least Mean Squared Error) linear and nonlinear estimators 
Week#9 
Introduction to random processes: basic concept, definition, classification, stationarity and independence, distribution and density functions 
Week#10 
Ergodic Theorem, correlation functions, introduction to Gaussian random processes 
Week#11 
Auto power spectral density of random processes: definition, properties, and relation to autocorrelation function 
Week#12 
Cross power spectral density, concept of white and colored noises 
Week#13 
Random signal response of linear systems: time domain and frequency domain characteristics, system evaluation 
Week#14 
Bandpass, bandlimited, and narrowband random processes: definition and characteristics 
Week#15 
Optimal linear systems: matched filter and Wiener filter 
 Final Examination  