MultivariateDiscreteDistribution Class 
Namespace: Accord.Statistics.Distributions.Multivariate
[SerializableAttribute] public abstract class MultivariateDiscreteDistribution : DistributionBase, IMultivariateDistribution, IDistribution, ICloneable, IMultivariateDistribution<int[]>, IDistribution<int[]>, IMultivariateDistribution<double[]>, IDistribution<double[]>, ISampleableDistribution<int[]>, IRandomNumberGenerator<int[]>, ISampleableDistribution<double[]>, IRandomNumberGenerator<double[]>, IFormattable
The MultivariateDiscreteDistribution type exposes the following members.
Name  Description  

MultivariateDiscreteDistribution 
Constructs a new MultivariateDiscreteDistribution class.

Name  Description  

Covariance 
Gets the variance for this distribution.
 
Dimension 
Gets the number of variables for this distribution.
 
Mean 
Gets the mean for this distribution.
 
Median 
Gets the median for this distribution.
 
Mode 
Gets the mode for this distribution.
 
Support 
Gets the support interval for this distribution.
 
Variance 
Gets the mean for this distribution.

Name  Description  

Clone 
Creates a new object that is a copy of the current instance.
(Inherited from DistributionBase.)  
ComplementaryDistributionFunction 
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point x.
This function is also known as the Survival function.
 
DistributionFunction 
Gets the cumulative distribution function (cdf) for
this distribution evaluated at point x.
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
Fit(Double) 
Fits the underlying distribution to a given set of observations.
 
Fit(Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
 
Fit(Double, Double) 
Fits the underlying distribution to a given set of observations.
 
Fit(Double, Int32) 
Fits the underlying distribution to a given set of observations.
 
Fit(Double, Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
 
Fit(Double, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
 
Generate 
Generates a random observation from the current distribution.
 
Generate(Double) 
Generates a random observation from the current distribution.
 
Generate(Int32) 
Generates a random vector of observations from the current distribution.
 
Generate(Int32) 
Generates a random observation from the current distribution.
 
Generate(Int32, Double) 
Generates a random vector of observations from the current distribution.
 
Generate(Int32, Int32) 
Generates a random vector of observations from the current distribution.
 
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
InverseDistributionFunction 
Not supported.
 
LogProbabilityMassFunction 
Gets the logprobability mass function (pmf) for
this distribution evaluated at point x.
 
MarginalDistributionFunction(Int32) 
Gets the marginal distribution of a given variable.
 
MarginalDistributionFunction(Int32, Int32) 
Gets the marginal distribution of a given variable evaluated at a given value.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ProbabilityMassFunction 
Gets the probability mass function (pmf) for
this distribution evaluated at point x.
 
ToString 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(IFormatProvider) 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(String) 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(String, IFormatProvider) 
Returns a String that represents this instance.
(Inherited from DistributionBase.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual  Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by Matrix.) 
A probability distribution identifies either the probability of each value of an unidentified random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous).
The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range.
The function describing the probability that a given discrete value will occur is called the probability function (or probability mass function, abbreviated PMF), and the function describing the cumulative probability that a given value or any value smaller than it will occur is called the distribution function (or cumulative distribution function, abbreviated CDF).
References: