(New page: There are several ways to create multi-variate data in matlab These generate random samples from a multivariate distribution - You can use mvnrnd(mu,sigma) function in Matlab. (See detai...)
 
 
(4 intermediate revisions by the same user not shown)
Line 2: Line 2:
  
 
These generate random samples from a multivariate distribution
 
These generate random samples from a multivariate distribution
−
- You can use mvnrnd(mu,sigma) function in Matlab. (See details below)
+
* You can use mvnrnd(mu,sigma) function in Matlab. (See details below)
−
- You can use the technique of [Generating Gaussian Samples] (which is good theory to know).
+
* You can use the technique of [Generating Gaussian Samples] (which is good theory to know).
  
−
This calculates the [pdf] function of the multivariate distribution
+
This calculates the [[pdf_Old Kiwi]] function of the multivariate distribution
−
- You can use `multigauss.m <multigauss.m>`_
+
* You can use `multigauss.m <multigauss.m>`_
  
  
−
Forum
+
'''From jin-young.kim.1 Tue Feb 12 11:54:56 -0500 2008
−
=========
+
−
 
+
−
From jin-young.kim.1 Tue Feb 12 11:54:56 -0500 2008
+
 
From: jin-young.kim.1
 
From: jin-young.kim.1
 
Date: Tue, 12 Feb 2008 11:54:56 -0500
 
Date: Tue, 12 Feb 2008 11:54:56 -0500
 
Subject: How to generate multivariate normal distribution using Matlab
 
Subject: How to generate multivariate normal distribution using Matlab
−
Message-ID: <20080212115456-0500@https://engineering.purdue.edu>
+
Message-ID: <20080212115456-0500@https://engineering.purdue.edu>'''
  
−
=> You can use mvnrnd(mu,sigma) function in Matlab.  
+
=> You can use mvnrnd(mu,sigma) function in Matlab.
  
 
::
 
::
  
−
  MVNRND Random vectors from the multivariate normal distribution.
+
MVNRND Random vectors from the multivariate normal distribution.
−
    R = MVNRND(MU,SIGMA) returns an N-by-D matrix R of random vectors
+
R = MVNRND(MU,SIGMA) returns an N-by-D matrix R of random vectors
−
    chosen from the multivariate normal distribution with mean vector MU,
+
chosen from the multivariate normal distribution with mean vector MU,
−
    and covariance matrix SIGMA.  MU is an N-by-D matrix, and MVNRND
+
and covariance matrix SIGMA.  MU is an N-by-D matrix, and MVNRND
−
    generates each row of R using the corresponding row of MU.  SIGMA is a
+
generates each row of R using the corresponding row of MU.  SIGMA is a
−
    D-by-D symmetric positive semi-definite matrix, or a D-by-D-by-N array.
+
D-by-D symmetric positive semi-definite matrix, or a D-by-D-by-N array.
−
    If SIGMA is an array, MVNRND generates each row of R using the
+
If SIGMA is an array, MVNRND generates each row of R using the
−
    corresponding page of SIGMA, i.e., MVNRND computes R(I,:) using MU(I,:)
+
corresponding page of SIGMA, i.e., MVNRND computes R(I,:) using MU(I,:)
−
    and SIGMA(:,:,I).  If MU is a 1-by-D vector, MVNRND replicates it to
+
and SIGMA(:,:,I).  If MU is a 1-by-D vector, MVNRND replicates it to
−
    match the trailing dimension of SIGMA.
+
match the trailing dimension of SIGMA.
−
+
 
−
    R = MVNRND(MU,SIGMA,N) returns a N-by-D matrix R of random vectors
+
R = MVNRND(MU,SIGMA,N) returns a N-by-D matrix R of random vectors
−
    chosen from the multivariate normal distribution with 1-by-D mean
+
chosen from the multivariate normal distribution with 1-by-D mean
−
    vector MU, and D-by-D covariance matrix SIGMA.
+
vector MU, and D-by-D covariance matrix SIGMA.
−
+
−
    Example:
+
−
      mu = [1 -1]; Sigma = [.9 .4; .4 .3];
+
−
      r = mvnrnd(mu, Sigma, 500);
+
−
      plot(r(:,1),r(:,2),'.');
+
  
−
+
Example:
−
    See also mvtrnd, mvnpdf, mvncdf, normrnd.
+
mu = [1 -1]; Sigma = [.9 .4; .4 .3];
 +
r = mvnrnd(mu, Sigma, 500);
 +
plot(r(:,1),r(:,2),'.');
  
 +
See also mvtrnd, mvnpdf, mvncdf, normrnd.
  
−
    Reference page in Help browser
+
Reference page in Help browser
−
      doc mvnrnd
+
doc mvnrnd
  
 
Ref: Matlab Help
 
Ref: Matlab Help
Line 53: Line 48:
 
Here is another way to do so (probably what mvnrnd.m is doing in the first place):  GeneratingGaussianSamples
 
Here is another way to do so (probably what mvnrnd.m is doing in the first place):  GeneratingGaussianSamples
  
−
From landis.m.huffman.1 Tue Feb 12 23:09:17 -0500 2008
+
'''From landis.m.huffman.1 Tue Feb 12 23:09:17 -0500 2008
 
From: landis.m.huffman.1
 
From: landis.m.huffman.1
 
Date: Tue, 12 Feb 2008 23:09:17 -0500
 
Date: Tue, 12 Feb 2008 23:09:17 -0500
 
Subject: Generating Gaussian Samples
 
Subject: Generating Gaussian Samples
−
Message-ID: <20080212230917-0500@https://engineering.purdue.edu>
+
Message-ID: <20080212230917-0500@https://engineering.purdue.edu>'''
  
−
I worked out a proof for using the Cholsky decomposition of the covariance matrix for [Generating Gaussian Samples].  I suppose you could use this if you were not going to use Matlab, which, as I have found here, already has a canned function for this type of sampling.
+
I worked out a proof for using the Cholsky decomposition of the covariance matrix for [[Generating Gaussian Samples_Old Kiwi]].  I suppose you could use this if you were not going to use Matlab, which, as I have found here, already has a canned function for this type of sampling

Latest revision as of 16:44, 19 March 2008

There are several ways to create multi-variate data in matlab

These generate random samples from a multivariate distribution

  • You can use mvnrnd(mu,sigma) function in Matlab. (See details below)
  • You can use the technique of [Generating Gaussian Samples] (which is good theory to know).

This calculates the pdf_Old Kiwi function of the multivariate distribution

  • You can use `multigauss.m <multigauss.m>`_


From jin-young.kim.1 Tue Feb 12 11:54:56 -0500 2008 From: jin-young.kim.1 Date: Tue, 12 Feb 2008 11:54:56 -0500 Subject: How to generate multivariate normal distribution using Matlab Message-ID: <20080212115456-0500@https://engineering.purdue.edu>

=> You can use mvnrnd(mu,sigma) function in Matlab.

MVNRND Random vectors from the multivariate normal distribution. R = MVNRND(MU,SIGMA) returns an N-by-D matrix R of random vectors chosen from the multivariate normal distribution with mean vector MU, and covariance matrix SIGMA. MU is an N-by-D matrix, and MVNRND generates each row of R using the corresponding row of MU. SIGMA is a D-by-D symmetric positive semi-definite matrix, or a D-by-D-by-N array. If SIGMA is an array, MVNRND generates each row of R using the corresponding page of SIGMA, i.e., MVNRND computes R(I,:) using MU(I,:) and SIGMA(:,:,I). If MU is a 1-by-D vector, MVNRND replicates it to match the trailing dimension of SIGMA.

R = MVNRND(MU,SIGMA,N) returns a N-by-D matrix R of random vectors chosen from the multivariate normal distribution with 1-by-D mean vector MU, and D-by-D covariance matrix SIGMA.

Example: mu = [1 -1]; Sigma = [.9 .4; .4 .3]; r = mvnrnd(mu, Sigma, 500); plot(r(:,1),r(:,2),'.');

See also mvtrnd, mvnpdf, mvncdf, normrnd.

Reference page in Help browser doc mvnrnd

Ref: Matlab Help

Here is another way to do so (probably what mvnrnd.m is doing in the first place): GeneratingGaussianSamples

From landis.m.huffman.1 Tue Feb 12 23:09:17 -0500 2008 From: landis.m.huffman.1 Date: Tue, 12 Feb 2008 23:09:17 -0500 Subject: Generating Gaussian Samples Message-ID: <20080212230917-0500@https://engineering.purdue.edu>

I worked out a proof for using the Cholsky decomposition of the covariance matrix for Generating Gaussian Samples_Old Kiwi. I suppose you could use this if you were not going to use Matlab, which, as I have found here, already has a canned function for this type of sampling

Retrieved from "https://www.projectrhea.org/rhea/index.php?title=Matlab_resources_Old_Kiwi&oldid=24431"
Shortcuts
Help Main Wiki Page Random Page Special Pages Log in
Search

Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett