PROBABILITY AND RANDOM PROCESS

This page has been produced for providing students with general informations and guidelines on the course of Probability and Random Process. 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

● Probabilty and Random Process (Undergraduate ICE2003 Class)

LECTURER

● Professor Joong Kyu Kim(Rm#: 21225, Tel: 031-290-7122)

COURSE OBJECTIVE

● To learn the basics on probability, random variables, and stochastic processes in order to apply the concepts to a wide range of electrical, and electronics engineering fields.

COURSE DESCRIPTION

● Basic concepts of probability theory. Random variables: discrete, continuous, and conditional probability distributions; averages; independence. Introduction to discrete and continuous random processes: wide sense stationarity, correlation, spectral density.

PREREQUISITE

● Engineering Statistics, Probability and Statistics, & MATLAB programming

TEXTBOOK

● Elements of Engineering Probability & Statistics by R.E.Ziemer

REFERENCE

● 공학용 확률통계 및 랜덤 프로세스 이론 (김 중 규 번역)

● Probabilty and Random Processes by W. B. Davenport Jr.

● Probablity, Random Variables, and Stochastic Processes by A. Papoulis

● Probability and Random Processes by A. Leon-Garcia

CLASSNOTE

● For your convenience, the classnote in PS and PDF forms will be distributed via this web-site. Visit Classnotes section, and download or print the classnote!

Note:
Also, in order for you to read and/or print the classnotes in PS form, you must download the viewer program "Ghostview" in your computer. Visit first thedownload site, and be sure to download the program before you get the classnotes!!!
You must acquire both of the "gsview 4.3" and "AFPL Ghostscript 8.00"!!!

GRADE POLICY

Mid-term Exam	30%
Final Exam	40%
Attendance	10%
Homework and Project	20%
Total	100%

Note:

● All the exams are closed books, but you may bring one page of A4 size hand-written reference sheet to each examination. (Illegal sheets will be confiscated at the place!!!)

● Attendance will be checked 5 times during the semester without advanced notice.

● No grade change will be permitted at the end of the semester. (e.g. C or D to F)

TOPICS TO COVER

● Introduction

● Fundamental Concepts of Probability

● Single Random Variables and Probability Distributions

● Probability Distributions for More Than One Random Variable

● Elementary Statistics, Empirical Probability Distributions, and More on Simulation

● Estimation Theory and Applications

● Engineering Decisions

● Reliability

● Introduction to Random Processes

● Random Processes Through Systems

WEEKLY SCHEDULE

Week No.	Detailed Topics
Week#1	Introduction of the course, objective of the class, simulation of random phenomenoa.
Week#2	Approaches to probability, probability axioms, set theorey, various probability relationships.
Week#3	Conditional probability, and statistical independence, total probability & Bayes' theorem, counting techniques, introdunction to random variable, probability distribution function.
Week#4	Common random variables and their distribution function's, transformation of single random variable.
Week#5	Mathematical expectation, characteristic function, bivariate random variables and their CDF & PDF, discrete random variable pairs.
Week#6	Conditional CDF & PDF, statistical independence of random variables, expectation of function of two random variables, joint Gaussian PDF.
*--- Mid-term Examination ---*
Week#7	Function(transformation) of two random variables, central limit theorem, week law of large numbers, multiple random variables.
Week#8	Elementary statistics, sample mean & variance, regression technique, statistical process control, empirical distribution function.
Week#9	Estimation theory, point and interval estimators, maximum likelihood(ML) technique, orthogonality principle.
Week#10	Decision theory, Bayes', classical and other decion strategies.
Week#11	Reliability, time-dependent reliability, system reliability, Weibull failure model.
Week#12	Introduction to random process:statistical description, autocorrelation, cross-correlation, and covariance functions Gaussian random process.
Week#13	Systems with random inputs: zero-memory non-linear system, fixed linear system.
Week#14	White noise, narroeband random process, linear mean squared error estimation.
Week#15	Reserved
*--- Final Examination ---*