November 2012
Intermediate to advanced
794 pages
22h 9m
English
book
Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications
by John J. Shynk
Content preview from Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications
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1.3 STATISTICAL SIGNAL PROCESSING WITH MATLAB®
Although continuous-time random processes and discrete-time random sequences are covered in this book, all simulation examples generated using MATLAB®1 are necessarily discrete-time sequences. In this section, we provide an overview of methods for generating random sequences, and of filters for modifying their properties and frequency-domain characteristics.
1.3.1 Random Number Generation
The random number generators in MATLAB provide a means for realizing a pseudorandom sequence 
where the samples across time instants are independent and thus uncorrelated. There are two basic random number generators:
1. randn(N,1). Generates an 
vector of numbers on 
from the standard Gaussian distribution with zero mean and unit variance.
vector of numbers on from the standard Gaussian distribution with zero mean and unit variance.
2. rand(N,1). Generates an 
vector of numbers from the continuous uniform distribution on 
.
vector of numbers from the continuous uniform distribution on .
Example histograms with samples for the two random number generators are shown in Figure 1.31, where n is the number of samples ...
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