MATLAB File Help: prtRvMixture
  prtRvMixture  Mixture Random Variable
    RV = prtRvMixture creates a prtRvMixture object with empty
    mixingProportions and components. These parameters can be set
    manually or by calling the MLE method.     
    The prtRvMixture class is used to implement mixtures of prtRvs. The
    base prtRv object must implement the weightedMle() method.
    RV = prtRvMixture(PROPERTY1, VALUE1,...) creates a prtRvMixture
    object RV with properties as specified by PROPERTY/VALUE pairs.
    A prtRvMixture object inherits all properties from the prtRv class.
    In addition, it has the following properties:
    components        - A vector of prtRv objects. The length of the
                        array specifies the number of components in the
                        mixture. The component RV objects must all have
                        the same dimensionality.
    mixingProportions - A discrete probability vector, representing the
                        probability of each component in the mixture.
   A prtRvMixture 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
        % Create a prtRvMixture object consistig of 2 multivariate
        % normal objects
        rv = prtRvMixture('components',repmat(prtRvMvn,1,2));
        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
prtRvMixture Mixture Random Variable 
Property Summary
components A vector of the components 
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.