MATLAB File Help: prtRvGmm
  prtRvGmm - Gaussian Mixture Model Random Variable
    RV = prtRvGmm creates a prtRvGmm object with empty
    mixingProportions and prtRvMvn components. These parameters can be
    set manually or by calling the MLE method. A prtRvGmm is a mixture
    of multi-variance normal random variables.
    RV = prtRvGmm(PROPERTY1, VALUE1,...) creates a prtRvGmm
    object RV with properties as specified by PROPERTY/VALUE pairs.
    A prtRvGmm object inherits all properties from the prtRv class.
    In addition, it has the following properties:
    nComponents         - A positive integer specifiying the number of
                          MVN components in the mixture.
    covarianceStructure - The covariance structure applied to each of
                          the prtRvMvn objects in the mixture. See prtRvMvn.
    covariancePool      - A logical specifying whether the components
                          should share a common covariance. If set to
                          true the covariance of the components are 
                          set to the weighted average of the maximum
                          likelihood estimate for the covariance 
                          matrices for the components.
    components          - An array of prtRvMvn objects.
    mixingProportions   - A discrete probability vector, representing
                          the probability of each component in the
   A prtRvGmm object inherits all methods from the prtRv class.
   The MLE method can be used to estimate the distribution parameters
   from data.
        ds = prtDataGenOldFaithful;      % Load a data set
        rv = prtRvGmm('nComponents',2);  % Specify 2 components
        rv = mle(rv,ds);                 % Compute the ML estimate
        plotPdf(rv);                     % Plot the estimated PDF
        hold on;
        plot(ds);                        % Overlay the original data
See also
Class Details
Superclasses prtRv
Sealed false
Construct on load false
Constructor Summary
prtRvGmm Gaussian Mixture Model Random Variable 
Property Summary
components The mixture components 
covariancePool Flag indicating whether or not to pool the covariance 
covarianceStructure The covariance structure 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isTrained Indicates if prtAction object has been trained. 
mixingProportions The mixing proportions 
nComponents The number of components 
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 Plot 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.