MATLAB File Help: prtRvIndependent
  prtRvIndependent  Independent random variables
    RV = prtRvIndependent creates a prtRvIndependent object.
    prtRvIndependent objects enable the training of independent
    versions of a base random variable type on each column of a data
    set.  By default, prtRvIndependent assumes Gaussian distributed
    random variables.
    RV = prtRvIndependent('baseRv', VALUE) specifies the type of RV to
    be trained on each column of input data.  VALUE must specify a
    valid prtRV class.  By default the baseRv field is a
    RV = prtRvIndependent(PROPERTY1, VALUE1,...) creates a
    prtRvIndependent object RV with properties as specified by
    A prtRvIndependent object inherits all properties from the prtRv
    class. In addition, it has the following properties:
    baseRv          - A prtRv object specifying the type of classifier
                      to create independent versions of.
    rvArray         - An array of objects of type baseRv that are
                      generated by calling the MLE method.  Can also be
                      manually set to specify particular parameters.
   A prtRvIndependent object inherits all methods from the prtRv class. 
   The MLE method can be used to estimate the distribution parameters from
   dataSet    = prtDataGenUnimodal;   % Load a dataset consisting of 2
                                      % classes
   % Extract one of the classes from the dataSet
   dataSetOneClass = prtDataSetClass(dataSet.getObservationsByClass(1));
   mvnRv = prtRvIndependent;            % Create a prtRvIndependent
                                        % object, with mvn components
   mvnRv = mvnRv.mle(dataSetOneClass);  % Compute the maximum
                                        % likelihood estimate from the
                                        % data
   indepRv = prtRvIndependent;          %Created an indepednent RV
                                        %(default baseRv is gaussian)
   indepRv = indepRv.mle(dataSetOneClass);
   subplot(2,2,1); mvnRv.plotPdf; 
   hold on; dataSetOneClass.plot;
   title('MVN RV Pdf');
   subplot(2,2,2); indepRv.plotPdf; 
   hold on; dataSetOneClass.plot;
   title('Independent Gaussian RV Pdf');
See also
Class Details
Superclasses prtRv, prtRvMemebershipModel
Sealed false
Construct on load false
Constructor Summary
prtRvIndependent Independent random variables 
Property Summary
baseRv The base random variable 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isTrained Indicates if prtAction object has been trained. 
rvArray The array of random variables 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  cdf Output the cdf of the random variable evaluated at the points specified 
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  draw Draw random samples from the distribution described by the prtRv object 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  logPdf Output the log pdf of the random variable evaluated at the points specified 
  mle Compute the maximum likelihood estimate 
  optimize Optimize action parameter by exhaustive function maximization. 
  pdf Output the pdf of the random variable evaluated at the points specified 
  plotCdf Plots the CDF of the prtRv 
  plotLogPdf Plot the pdf of the prtRv 
  plotPdf Plot the pdf of the prtRv 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.