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Unformatted text preview: 19 Example #3  Rayleigh pdf 20 Rayleigh pdf 21 Probability Mass Functions (pmf) 22 Example #1: Binary Distribution 23 Example #2: Binomial Distribution 24 Example #2 (continued) 25 Central Limit Theorem 26 Example of Central Limit Theorem: 27 Random Processes 28 Terminology Describing Random Processes 29 Description of Random Processes ± Knowing the pdf of individual samples of the random process is not sufficient. We also need to know how how individual samples are related to each other. ± Two tools are available to describe this relationship: ² Autocorrelation function ² Power spectral density function 30 Autocorrelation 31 Power Spectral Density 32 Gaussian Random Processes ± Gaussian Random Processes have several special properties: ² If a Gaussian random process is widesense stationary, then it is also stationary. ² Any sample point from a Gaussian random process is a Gaussian random variable. ² If the input to a linear system is a Gaussian random process, then the output is also a Gaussian random process. 33 Linear Systems 34 Computing the Output of Linear Systems...
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This note was uploaded on 11/23/2010 for the course EE EE522 taught by Professor Eeehwangsoo during the Spring '10 term at Korea Advanced Institute of Science and Technology.
 Spring '10
 EeeHwangsoo

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