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| + | =ECE 302 List= | ||
| − | + | '''Topics Covered''': An introductory treatment of probability theory including distribution and density functions, moments and random variables. Applications of normal and exponential distributions. Estimation of means, variances. Correlation and spectral density functions. Random processes and response of linear systems to random inputs.<br/><br/> | |
| + | '''Course Outcomes''':<br/><br/> | ||
| + | i. an ability to solve simple probability problems in electrical and computer engineering applications. | ||
| + | <br/>ii. an ability to model complex families of signals by means of random processes. | ||
| + | <br/>iii. an ability to determine the random process model for the output of a linear system when the system and input random process models are known. <br/><br/> | ||
| − | + | '''Concepts''':<br/><br/> | |
| + | 1. Experiments and Outcomes (discrete and continuous), probability of outcomes | ||
| + | <br/><br/>2. Sets, subsets, axiomatic approach, properties of probability, conditional probability | ||
| + | <br/><br/>3. Independence, Cumulative Distribution Function (used in ECE 438), Probability Density Function (used in ECE 438), Probability Mass Function, functions of random variables | ||
| + | <br/><br/>4. Density methods, distribution methods, mean values, moments | ||
| + | <br/><br/>5. Expected value (used in ECE 438), variance (used in ECE 438) | ||
| + | <br/><br/>6. Conditional/total pdf, pmf, expectation, variance | ||
| + | <br/><br/>7. Poisson process, Bernoulli process | ||
| + | <br/><br/>8. Two random variables, correlation, jointly-gaussian random variables | ||
| + | <br/><br/>9. Signal detection, signal estimation, cross-correlation functions<br/><br/> | ||
| − | [[ Snikitha | + | [https://kiwi.ecn.purdue.edu/rhea/index.php/Snikitha_ECECourseList ECE Course List] |
| + | [https://kiwi.ecn.purdue.edu/rhea/index.php/User:Snikitha Snikitha] | ||
Revision as of 07:19, 4 May 2010
ECE 302 List
Topics Covered: An introductory treatment of probability theory including distribution and density functions, moments and random variables. Applications of normal and exponential distributions. Estimation of means, variances. Correlation and spectral density functions. Random processes and response of linear systems to random inputs.
Course Outcomes:
i. an ability to solve simple probability problems in electrical and computer engineering applications.
ii. an ability to model complex families of signals by means of random processes.
iii. an ability to determine the random process model for the output of a linear system when the system and input random process models are known.
Concepts:
1. Experiments and Outcomes (discrete and continuous), probability of outcomes
2. Sets, subsets, axiomatic approach, properties of probability, conditional probability
3. Independence, Cumulative Distribution Function (used in ECE 438), Probability Density Function (used in ECE 438), Probability Mass Function, functions of random variables
4. Density methods, distribution methods, mean values, moments
5. Expected value (used in ECE 438), variance (used in ECE 438)
6. Conditional/total pdf, pmf, expectation, variance
7. Poisson process, Bernoulli process
8. Two random variables, correlation, jointly-gaussian random variables
9. Signal detection, signal estimation, cross-correlation functions
