Download CS602 Stochastic Systems I

CS 602/CMPE 602: Stochastic Systems-I
Year:
2003
Office Ext. & Email: 2125, [email protected]
Quarter:
Autumn
Office Hours:
Category:
Graduate
Instructor’s Name:
Asim Loan
Tuesday and Thursday, 11:45-12:45 p.m. and 3:30-4:30 p.m.
TA for the Course:
Course Code
(Units)
Course
Description
TBA
CS 602/ CMPE 602 (3 Units)
This is a graduate level course in probability and random variables that is a pre-requisite
for almost all graduate level courses in communications, signal processing, controls and
networks. The course will cover axiomatic foundations of probability, random variables,
distributions, densities, functions of a random variable, functions of several random
variables, moment generating functions and sequences of random variables.
Core/Elective
Core for Computer Engineering Majors and Elective for Computer Science Majors
Pre-requisites
Graduate standing
Goals




To understand elements of probability theory and its application to various problems
in engineering.
Become familiar with discrete and continuous probability distributions.
Be able to transform, compute densities and expectations of (a) one random variable
and (b) a sequence of random variables.
Become familiar with the moment theory.
CS 602/CMPE 602: Stochastic Systems-I
Textbooks,
Programming
Environment
Lectures,
Tutorials &
Attendance
Policy
Grading
Year:
2003
Quarter:
Autumn
REQUIRED TEXTS:
Probability, Random Variables and Stochastic Processes by Athanasios Papoulis, McGraw
Hill
Other Reference:
Probability and Random Processes for Electrical Engineering by Alberto Leon Garcia,
Addison-Wesley
There will be 19 sessions
 75 minutes each
Attendance is strongly recommended since there will be frequent in-class surprise quizzes.
Homework
Quiz
Midterm
Final Exam
10%
15%
35%
40%
CS 602/CMPE 602: Stochastic Systems-I
Module
2003
Quarter:
Autumn
Sessions
Readings
Basic Concepts
Axiomatic Probability Theory
Discrete Probability Space
Independent Events
1
Ch. 2
Repeated Trials
Single and Multiple Events
Distributions – Binomial and Gaussian
2
Ch. 3
2
Random Variables
Distribution and Density Functions
Conditional density functions
3
Ch. 4
3
Functions of A Random Variable, Y = g(X)
Distribution and Density Functions
Conditional density functions
4
Ch. 5
4
Functions of Two Random Variables, Z = g(X, Y)
Distribution and Density Functions
Conditional density functions
3
Ch. 6
5
Moment Generating Functions
First and second order Moments
Conditional Moments
Characteristic Functions
4
Ch. 7
6
Sequence of Random Variables
Sum, Product
2
Ch. 8
1
Topics
Year: