MATLAB File Help: prtRvDiscrete
  prtRvDiscrete  Discrete random variable.
    RV = prtRvDiscrete creates a prtRvDiscrete object with an unknown
    symbols and unspecified probabilities. These properties
    can be set manually or by using the MLE method. Both of these
    properties must be set to make the RV valid.
    RV = prtRvDiscrete(PROPERTY1, VALUE1,...) creates a
    prtRvDiscrete object RV with properties as specified by
    A prtRvDiscrete object inherits all properties from the
    prtRv class. In addition, it has the following properties:
    nCategories   - number of integers modeled by the RV
    probabilities - 1 x nCategories vector of doubles less than 1
                    that sum to 1, representing the probability of
                    each of the integers
    symbols       - nCategories x M matrix of symbols
                    M specifies the dimensionality of the RV object.
   A prtRvDiscrete object inherits all methods from the prtRv
   class. The MLE  method can be used to set the parameters from data.
       rv = prtRvDiscrete('symbols',(10:12)','probabilities',[0.3 0.3 0.4]);
       % Plot MVN distributed data as discrete
       rv = mle(prtRvDiscrete,draw(prtRvMvn('mu',[1 2],'sigma',2*eye(2)),100));
See also
Class Details
Superclasses prtRv, prtRvMemebershipModel
Sealed false
Construct on load false
Constructor Summary
prtRvDiscrete Discrete random variable. 
Property Summary
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isTrained Indicates if prtAction object has been trained. 
nCategories The number of categories 
probabilities The probabilities of each symbol 
symbols The symbols 
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.