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<div style="border-bottom: rgb(68,68,136) 1px solid; text-align: center; border-left: rgb(51,51,136) 4px solid; padding-bottom: 2em; margin: auto; padding-left: 2em; width: 30em; padding-right: 2em; background: rgb(238,238,255); border-top: rgb(68,68,136) 1px solid; border-right: rgb(68,68,136) 1px solid; padding-top: 2em"> | <div style="border-bottom: rgb(68,68,136) 1px solid; text-align: center; border-left: rgb(51,51,136) 4px solid; padding-bottom: 2em; margin: auto; padding-left: 2em; width: 30em; padding-right: 2em; background: rgb(238,238,255); border-top: rgb(68,68,136) 1px solid; border-right: rgb(68,68,136) 1px solid; padding-top: 2em"> | ||
Message Area: | Message Area: | ||
| − | * | + | *Sample quizzes has been uploaded to help you to review the final exam! |
| + | *Homework 7 solution has been posted. | ||
</div> | </div> | ||
== Course Information == | == Course Information == | ||
| Line 25: | Line 26: | ||
**Test 1: Friday February 8, 2013. | **Test 1: Friday February 8, 2013. | ||
**Test 2: Wednesday April 3, 2013. | **Test 2: Wednesday April 3, 2013. | ||
| − | **Final: | + | **Final: Wednesday May 1, 2013 |
---- | ---- | ||
| Line 42: | Line 43: | ||
:[[Lecture33_blog_ECE302S13_Boutin|33]],[[Lecture34_blog_ECE302S13_Boutin|34]],[[Lecture35_blog_ECE302S13_Boutin|35]],[[Lecture36_blog_ECE302S13_Boutin|36]],[[Lecture37_blog_ECE302S13_Boutin|37]],[[Lecture38_blog_ECE302S13_Boutin|38]],[[Lecture39_blog_ECE302S13_Boutin|39]],[[Lecture40_blog_ECE302S13_Boutin|40]],[[Lecture41_blog_ECE302S13_Boutin|41]],[[Lecture42_blog_ECE302S13_Boutin|42]],[[Lecture43_blog_ECE302S13_Boutin|43]],[[Lecture44_blog_ECE302S13_Boutin|44]] | :[[Lecture33_blog_ECE302S13_Boutin|33]],[[Lecture34_blog_ECE302S13_Boutin|34]],[[Lecture35_blog_ECE302S13_Boutin|35]],[[Lecture36_blog_ECE302S13_Boutin|36]],[[Lecture37_blog_ECE302S13_Boutin|37]],[[Lecture38_blog_ECE302S13_Boutin|38]],[[Lecture39_blog_ECE302S13_Boutin|39]],[[Lecture40_blog_ECE302S13_Boutin|40]],[[Lecture41_blog_ECE302S13_Boutin|41]],[[Lecture42_blog_ECE302S13_Boutin|42]],[[Lecture43_blog_ECE302S13_Boutin|43]],[[Lecture44_blog_ECE302S13_Boutin|44]] | ||
*Week(17) Final Exam Week | *Week(17) Final Exam Week | ||
| + | :Final exam is on Wednesday May 1, 1:00p - 3:00p STEW 130 | ||
---- | ---- | ||
| Line 75: | Line 77: | ||
*Random Processes | *Random Processes | ||
**[[Practice_Question_what_is_stationary_increment_property_sum_process_ECE302S13|the stationary increment property of a sum process]] | **[[Practice_Question_what_is_stationary_increment_property_sum_process_ECE302S13|the stationary increment property of a sum process]] | ||
| + | ---- | ||
| + | ==Quizzes== | ||
| + | *[[quiz1_set_definition_ECE302_S13_Boutin|Quiz 1 on the definition of a set]] | ||
| + | *[[quiz2_set_operation_prove_De_Morgan_Law_ECE302_S13_Boutin|Quiz 2 on proving De Morgan's law]] | ||
| + | *[[quiz3_set_theory_probability_S13_Boutin|Quiz 3 set theoretic probability]] | ||
| + | *[[quiz4_expectation_discrete_RV_ECE302_S13_Boutin|Quiz 4 Expectation of discrete random variable]] | ||
| + | *[[quiz5_cdf_GaussianRV_ECE302_S13_Boutin|Quiz 5 cdf of Normal random variable (part1)]] | ||
| + | *[[quiz6_cdf_GaussianRV2_ECE302_S13_Boutin|Quiz 6 cdf of Normal random variable (part2)]] | ||
| + | *[[quiz7_expectation_continuousRV_ECE302_S13_Boutin|Quiz 7 Expectation of continuous random variable]] | ||
| + | *[[quiz8_Poisson_process_ECE302_S13_Boutin|Quiz 8 Poisson Process]] | ||
---- | ---- | ||
==Homework== | ==Homework== | ||
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*[[hw4_discussion_sln_ECE302_S13_Boutin|HW4_solution]] | *[[hw4_discussion_sln_ECE302_S13_Boutin|HW4_solution]] | ||
*[[hw6_discussion_sln_ECE302_S13_Boutin|HW6_solution]] | *[[hw6_discussion_sln_ECE302_S13_Boutin|HW6_solution]] | ||
| + | *[[hw7_discussion_sln_ECE302_S13_Boutin|HW7_solution]] | ||
---- | ---- | ||
== Relevant Resources == | == Relevant Resources == | ||
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|- | |- | ||
| 2 | | 2 | ||
| − | | | + | | [[Applications of Poisson Random Variables|Applications of Poisson Random Variables]] |
| − | | | + | | Trevor Holloway |
|- | |- | ||
| 3 | | 3 | ||
| Line 158: | Line 171: | ||
|- | |- | ||
| 7 | | 7 | ||
| − | | [[ECE302S13Notes| | + | | [[ECE302S13Notes|TF101 Slectures]] |
| Bob Wayner | | Bob Wayner | ||
| + | |- | ||
| + | | 8 | ||
| + | | [[CorrelationvsCovariance|Correlation vs. Covariance]] | ||
| + | | Blue | ||
|} | |} | ||
---- | ---- | ||
[[List_of_Course_Wikis|Back to List of Course Wikis]] | [[List_of_Course_Wikis|Back to List of Course Wikis]] | ||
Latest revision as of 19:14, 30 April 2013
Rhea Section for ECE302, Professor Boutin, Spring 2013
MWF 12:30- 1:20pm in MSEE B012
Message Area:
- Sample quizzes has been uploaded to help you to review the final exam!
- Homework 7 solution has been posted.
Course Information
- Instructor: Prof. Mimi
- Office: MSEE 342
- Office hours are listed here.
- 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: Wednesday May 1, 2013
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):
- Week(5-8)
- Week(9-12) <Week 10- Spring Break!>
- Week(13-16)
- Week(17) Final Exam Week
- Final exam is on Wednesday May 1, 1:00p - 3:00p STEW 130
Collectively Solved Practice Problems
- Definition of a set
- Set operations
- Conditional Probability
- Discrete Random Variables
- Continuous random variables
- Normalizing the probability mass function of a Gaussian random variable
- Obtaining the joint pdf from the marginal pdfs of two independent variables
- Compute a probability
- Find the CDF
- Compute the mean
- Compute the zero-th order moment of a Gaussian
- Compute the first order moment of a Gaussian
- Compute the second order moment of a Gaussian
- Comparing probabilities for different Gaussians
- Compute the probability that a meeting will occur
- Find the conditional probability density function
- Find the conditional probability density function (again)
- Find the conditional probability density function (conditioned on an event this time)
- Determine if X and Y independent from their joint density
- Recover the pmf corresponding to this characteristic function
- Obtain the characteristic function of an exponential random variable
- pdf of Y=aX+b
- Various Questions about a 2D Gaussian
- Random Processes
Quizzes
- Quiz 1 on the definition of a set
- Quiz 2 on proving De Morgan's law
- Quiz 3 set theoretic probability
- Quiz 4 Expectation of discrete random variable
- Quiz 5 cdf of Normal random variable (part1)
- Quiz 6 cdf of Normal random variable (part2)
- Quiz 7 Expectation of continuous random variable
- Quiz 8 Poisson Process
Homework
- HW1
- HW2
- HW3
- HW4
- HW5
- HW6
- HW7 (Includes all practice problems assigned after the lecture-- see lecture blogs )
Homework Discussion and Solutions
Relevant Resources
- Practice Problems on Probability
- Rhea's Collective Table of Formulas.
- A tutorial on Counting Partitions or Subsets, by Math Squad member Steve Mussmann
- A tutorial about Bayes' Theorem, by Math Squad member Maliha Hossain
Honors Projects
Your turn! Bonus Point opportunities
Exercises
- Invent a problem on conditional probability or independence and share it with your classmates (0.5% course grade bonus)
- Find a mistake in your classmates' solutions (0.5% course grade bonus)
- Invent a problem on expectation and/or variance of a discrete random variable (0.5% course grade bonus)
- Share your solution of hw 5 (0.5% course grade bonus)
- Explain the stationary increment property of sum processes (0.5% course grade bonus)
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 |
|---|---|---|
| 0 | Project Template | |
| 1 | Methods of generating random variables | Zhenming Zhang |
| 2 | Applications of Poisson Random Variables | Trevor Holloway |
| 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 |
| 7 | TF101 Slectures | Bob Wayner |
| 8 | Correlation vs. Covariance | Blue |
