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ELE 391 Random Signals
Spring 2016
Homework 1: (Due 9:30AM on Tuesday Feb 9)
(Only four problems will be graded chosen at random)
Problems from end of Chapter 1
Work Problems 1, 5, 7,
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ELE 391 Random Signals
Spring 2016
Homework 2: (Due 9:30AM on Thursday Feb 11)
(Only four problems will be graded chosen at random)
Problems from end of Chapter 2
Work Problems 1, 3,
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ELE 391 Random Signals
Spring 2016
Homework 4: (Due 9:30AM on Thursday Feb 25)
(Only four problems will be graded selected at random)
Problems from end of Chapter 3
Solve Problems 10,
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ELE 391 Random Signals
Spring 2016
Homework 3: (Due 9:30AM on Thursday Feb 18)
(Only four problems will be graded selected at random)
Problems from end of Chapter 3
Solve Problems 1,
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ELE 391 Random Signals
Spring 2016
Homework 2: (Due 9:30AM on Thursday Feb 11)
(Only four problems will be graded chosen at random)
Problems from end of Chapter 2
Problem 1: 96
Proble
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ELE 391 Random Signals
Spring 2016
Homework 3: (Due 9:30AM on Thursday Feb 18)
(Only four problems will be graded selected at random)
Problems from end of Chapter 3
Problem 1
a- Rxcfw
Chapter 1
Bayes Rule
Introduction
What is Probability?
Review of Set
Theory
This rule allows us to calculate P(B|A) from P(A|B).
For two events A and B, we have
Review
Venn Diagrams
Set Operations
Car
Chapter 2
Unordered Sampling with Replacement
Finding
Probabilities Using
Counting Methods
This is the most challenging type of sampling.
Lemma: The total number of distinct k samples from
an n-elemen
Hypergeometric Distribution
Chapter 3
Basic Concepts
You have a bag that contains b blue marbles and r red
marbles.
Choose k b + r marbles at random without
replacement.
Let X be the number of blue ma
Chapter 2
Finding Probabilities Using Counting Methods
Finding
Probabilities Using
Counting Methods
Counting methods that can be used for discrete sample
spaces with equally likely outcomes.
For such
Chapter 4
Functions of Continuous Random Variables
Introduction
Continuous
Random Variables
and their
Distributions
Example: Let X be a Uniform(0, 1) random variable,
and let Y = e X .
Probability
Dis
Chapter 3
Cumulative Distribution Function (CDF)
Basic Concepts
Cumulative distribution function (CDF) of a random
variable is a method to describe the distribution of
random variables.
The advantage
Syllabus for ELE 391
Spring 2016
EL E 391 Random Signals
Spring 2016
Time/Location:
T Th 09:30 10:45, Anderson Room 235
Instructor:
Dr. Mustafa M. Matalgah
Office: Anderson Hall 12, Phone: 915-5381, E
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7H J
Instructor: Dr. R. Viswanathan "' ELE 391 Random Signals Time: 75 minutes
Spring 2013 Exam 3 Closed Book, Closed Notes Maximum POintS 15
r r'f : 5 y L " l '
1. Conside
ISU'UCtOIZ Dr. R, Viswanathan ELE 391 Random Signals Time: 75 minutes
Spring 2014 Exam 2 Closed Book, Closed Notes Maximum Points 50
1 . . . . _ _ 0.3 x=1.2
. e a iscrete random variable wrth probabil
PRINT NAME
GRADE
ELE 391 Random Signals
Spring 2016
Homework 1: (Due 9:30AM on Tuesday Feb 9)
(Only four problems will be graded chosen at random)
Problems from end of Chapter 1
Work Problems 1, 5, 7,