This preview shows pages 1–3. Sign up to view the full content.
Probability and Stochastic Processes
A Friendly Introduction for Electrical and Computer Engineers
SECOND EDITION
MATLAB Function Reference
Roy D. Yates and David J. Goodman
May 22, 2004
This document is a supplemental reference for MATLAB functions described in the text
Prob
ability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers
.
This document should be accompanied by
matcode.zip
, an archive of the corresponding MAT
LAB
.m
files. Here are some points to keep in mind in using these functions.
•
The actual programs can be found in the archive
matcode.zip
or in a directory
matcode
.
To use the functions, you will need to use the MATLAB command
addpath
to add this
directory to the path that MATLAB searches for executable
.m
files.
•
The
matcode
archive has both general purpose programs for solving probability problems
as well as specific
.m
files associated with examples or quizzes in the text. This manual
describes only the general purpose
.m
files in
matcode.zip
. Other programs in the archive
are described in main text or in the
Quiz Solution Manual
.
•
The MATLAB functions described here are intended as a supplement the text. The code is
not fully commented. Many comments and explanations relating to the code appear in the
text, the
Quiz Solution Manual
(available on the web) or in the
Problem Solution Manual
(available on the web for instructors).
•
The code is instructional. The focus is on MATLAB programming techniques to solve prob
ability problems and to simulate experiments. The code is definitely not bulletproof; for
example, input range checking is generally neglected.
•
This is a work in progress.
At the moment (May, 2004), the homework solution manual has
a number of unsolved homework problems. As these solutions require the development of
additional MATLAB functions, these functions will be added to this reference manual.
•
There is a nonzero probability (in fact, a probability close to unity) that errors will be found. If
you find errors or have suggestions or comments, please send email to
[email protected]
.
When errors are found, revisions both to this document and the collection of MATLAB func
tions will be posted.
1
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document Functions for Random Variables
bernoullipmf
y=bernoullipmf(p,x)
function pv=bernoullipmf(p,x)
%For Bernoulli (p) rv X
%input = vector x
%output = vector pv
%such that pv(i)=Prob(X=x(i))
pv=(1p)*(x==0) + p*(x==1);
pv=pv(:);
Input:
p
is the success probability of a Bernoulli
random variable
X
,
x
is a vector of possible
sample values
Output:
y
is a vector with
y(i)
=
P
X
(
x(i)
)
.
bernoullicdf
y=bernoullicdf(p,x)
function cdf=bernoullicdf(p,x)
%Usage: cdf=bernoullicdf(p,x)
% For Bernoulli (p) rv X,
%given input vector x, output is
%vector pv such that pv(i)=Prob[X<=x(i)]
x=floor(x(:));
allx=0:1;
allcdf=cumsum(bernoullipmf(p,allx));
okx=(x>=0); %x_i < 1 are bad values
x=(okx.*x); %set bad x_i=0
cdf= okx.*allcdf(x); %zeroes out bad x_i
Input:
p
This is the end of the preview. Sign up
to
access the rest of the document.
This homework help was uploaded on 04/09/2008 for the course ENGR, STAT 320, 262, taught by Professor Harris during the Spring '08 term at Purdue University.
 Spring '08
 Harris

Click to edit the document details