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*2.1 Definition and examples | *2.1 Definition and examples | ||
*2.2 Functions of a discrete random variable | *2.2 Functions of a discrete random variable | ||
| − | *2.3 Moments of discrete random variable | + | *2.3 Moments of discrete random variable (expectation, variance) |
*2.4 Conditioning of a discrete random variable | *2.4 Conditioning of a discrete random variable | ||
*2.5 Independence of discrete random variables | *2.5 Independence of discrete random variables | ||
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==Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)== | ==Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)== | ||
| − | Week (6)7- ? (Lecture ([[Lecture17_blog_ECE302S13_Boutin|17]]) [[Lecture18_blog_ECE302S13_Boutin|18]] | + | Week (6)7- ? (Lecture ([[Lecture17_blog_ECE302S13_Boutin|17]]) [[Lecture18_blog_ECE302S13_Boutin|18]],[[Lecture19_blog_ECE302S13_Boutin|19]],[[Lecture20_blog_ECE302S13_Boutin|20]],... ) |
*3.1 Definition of continuous random variable, probability density function. | *3.1 Definition of continuous random variable, probability density function. | ||
| − | + | *3.2 Moments of a continuous random variables (expectation, variance) | |
| − | + | *3.3 The cumulative distribution function of a random variable (discrete or continuous) | |
| − | + | *3.4 Normally distributed random variables. | |
| + | *3.5 Focus on 2D random variables: expectation, conditioning, and independence. | ||
| + | *3.6 Function of a random variable | ||
| + | *3.7 Moment Generating (Characteristic) Function (for 1D discrete/continuous random variables) | ||
| + | *3.8 Pairs of jointly Gaussian Variables | ||
Suggested References | Suggested References | ||
:Chapter 4,5,6 in the textbook | :Chapter 4,5,6 in the textbook | ||
| − | + | :This [http://www.athenasc.com/Bivariate-Normal.pdf tutorial on the bivariate normal] (from a supplement to "[http://www.athenasc.com/probbook.html Introduction to Probability]," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6). | |
---- | ---- | ||
| − | ==Part 4: To be tested in the final exam== | + | ==Part 4: Random Processes (To be tested in the final exam)== |
Week 11-15 | Week 11-15 | ||
| − | * | + | *4.1 Introduction, Definition of Random Processes (CT and DT) |
| + | *4.2 Characteristics of Random Processes | ||
| + | *4.3 Examples of DT Random Processes; Sum Processes | ||
| + | *4.4 The Poisson Random Process and its relationship to Binomial Counting | ||
| + | *4.5 LTI systems and Random Processes | ||
| + | |||
| + | :Chapter 9,10 in the textbook. | ||
---- | ---- | ||
[[2013_Spring_ECE_302_Boutin|Back to ECE302 Spring 2013 Prof. Boutin]] | [[2013_Spring_ECE_302_Boutin|Back to ECE302 Spring 2013 Prof. Boutin]] | ||
Latest revision as of 10:18, 17 April 2013
Contents
- 1 ECE302 Course Outline, Spring 2013, Prof. Boutin
- 1.1 Part 1: Foundations (To be tested in the first intra-semestrial exam)
- 1.2 Part 2: Discrete Random Variables (To be tested in the second intra-semestrial exam)
- 1.3 Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)
- 1.4 Part 4: Random Processes (To be tested in the final exam)
ECE302 Course Outline, Spring 2013, Prof. Boutin
Part 1: Foundations (To be tested in the first intra-semestrial exam)
Week 1-3 (Lecture 1, 2, 3, 4, 5, 6, 7, 8, 9)
- 1.1 Sets
- Definition
- Operations
- De Morgan's Law
- 1.2 Probability Models
- Sample spaces
- Probability Laws (axioms, properties
- 1.3 Conditional Probabilities
- 1.4 Independence
- 1.5 Bernoulli Trials
- 1.6 Counting
Suggested references:
- Chapter 1 and 2 of the textbook,
- Chapter 1 of "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
- Foundations of Probability Theory: Basic Definitions, module by Don Johnson posted on Connexions
Part 2: Discrete Random Variables (To be tested in the second intra-semestrial exam)
Week 4-5(6) (Lecture 10,11, 12, 13, 14, 15, 16, (17) )
- 2.1 Definition and examples
- 2.2 Functions of a discrete random variable
- 2.3 Moments of discrete random variable (expectation, variance)
- 2.4 Conditioning of a discrete random variable
- 2.5 Independence of discrete random variables
Suggested References
- Chapter 3 in the textbook
- Chapter 2 in "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
- Chapter 4 of Collaborative Statistics by Illowski and Dean (available online)
Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)
Week (6)7- ? (Lecture (17) 18,19,20,... )
- 3.1 Definition of continuous random variable, probability density function.
- 3.2 Moments of a continuous random variables (expectation, variance)
- 3.3 The cumulative distribution function of a random variable (discrete or continuous)
- 3.4 Normally distributed random variables.
- 3.5 Focus on 2D random variables: expectation, conditioning, and independence.
- 3.6 Function of a random variable
- 3.7 Moment Generating (Characteristic) Function (for 1D discrete/continuous random variables)
- 3.8 Pairs of jointly Gaussian Variables
Suggested References
- Chapter 4,5,6 in the textbook
- This tutorial on the bivariate normal (from a supplement to "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6).
Part 4: Random Processes (To be tested in the final exam)
Week 11-15
- 4.1 Introduction, Definition of Random Processes (CT and DT)
- 4.2 Characteristics of Random Processes
- 4.3 Examples of DT Random Processes; Sum Processes
- 4.4 The Poisson Random Process and its relationship to Binomial Counting
- 4.5 LTI systems and Random Processes
- Chapter 9,10 in the textbook.
