Line 49: Line 49:
 
*Discrete Random Variables
 
*Discrete Random Variables
 
**[[Practice_Question_probability_normalization_ECE302S13Boutin|Normalizing the probability mass function of a discrete random variable]]
 
**[[Practice_Question_probability_normalization_ECE302S13Boutin|Normalizing the probability mass function of a discrete random variable]]
 +
*Continuous random variables
 +
**[[Practice_Question_gaussian_normalization_ECE302S13Boutin|Normalizing the probability mass function of a Gaussian random variable]]
 +
**[[Practice_Question_joint_from_marginals_independent_variables_ECE302S13Boutin|Obtaining the joint pdf from the marginal pdfs of two independent variables]]
 
----
 
----
 
==Homework==
 
==Homework==
Line 61: Line 64:
 
*[[hw3_discussion_sln_ECE302_S13_Boutin|HW3_solution]]
 
*[[hw3_discussion_sln_ECE302_S13_Boutin|HW3_solution]]
 
*[[hw4_discussion_sln_ECE302_S13_Boutin|HW4_solution]]
 
*[[hw4_discussion_sln_ECE302_S13_Boutin|HW4_solution]]
----
 
==Materials Covered==
 
* Probability Models (Ch.1)
 
* Axioms of Probability, counting, conditional probability, independence(Ch.2)
 
* Random variables, Expected value and moments, probability mass function (Ch.3)
 
* Cumulative distribution function, functions of a random variable (Ch.4)
 
* Two random variables(r.v.), joint cdf of 2 r.v., independence of 2 r.v., conditional expectation (Ch.5)
 
* Vector r.v., jointly Gaussian r.v, estimation of r.v. (Ch.6)
 
* Definition of Random Processes(r.p.), Poison r.p., Random Walk (Ch.9)
 
* Power spectral density, response of linear systems to random signals (Ch.10)
 
 
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----
 
== Relevant Resources  ==
 
== Relevant Resources  ==

Revision as of 07:14, 22 February 2013


Rhea Section for ECE302, Professor Boutin, Spring 2013

MWF 12:30- 1:20pm in MSEE B012

Message Area:

Don't forget to try out the following practice problem on pmf normalization.

Course Information

  • Instructor: Prof. Mimi
  • Teaching Assistant: Wei-Kang Hsu
    • Email: hsu59 at purdue dot you know what
    • Office hours: TF 2:00-4:00 pm EE209
  • Schedule
  • Course Syllabus
  • Important Dates:
    • Test 1: Friday February 8, 2013.
    • Test 2: Wednesday April 3, 2013.
    • Final: TBD

Textbook

Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Edition, by Alberto Leon-Garcia, Pearson Education, Inc., 2008, ISBN 0-13-601641-3


Lecture Blog

  • Week(1-4):
1, 2, 3, 4,5, 6, 7,8,9,10,11
  • Week(5-8)
12,13,14,15,16,17,18,19,20,21,22,23
  • Week(9-12)
24,25,26,27,28,29,30

Collectively Solved Practice Problems


Homework

  • HW1
  • HW2
  • HW3
  • HW4(Will include all problems assigned after the lecture-- see lecture blogs )

Homework Discussion and Solutions


Relevant Resources


Honors Projects


Your turn! Bonus Point opportunities

Exercises

  1. Invent a problem on conditional probability or independence and share it with your classmates (0.5% course grade bonus)
  2. Find a mistake in your classmates' solutions (0.5% course grade bonus)
  3. Invent a problem on expectation and/or variance of a discrete random variable

Class Project

Students in ECE302 Spring 2013 have the opportunity to earn up to a 3% bonus by contributing a Rhea page on a subject related to probability. To pick a subject, simply write your name next to it. Your page will be graded based on content as well as interactions with other people (page views, comments/questions on the page, etc.). The number of links to other courses and subjects will also be taken into account: the more the merrier! Please do not simply copy the lecture notes and do not plagiarize. Read Rhea's copyright policy before proceeding.

Topic Number Topic Description Student Name/nickname
1 Something related to generating random variables Name
2 Something related to Poisson random variables Name
3 Something related to Exponential random variables Name
4 Something related to Gaussian random variables Name
5 Something related to the Estimation of random variables Name
6 Automatic music composition project Name

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Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman