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

MultivariateContinuousDistribution 
Constructs a new MultivariateDistribution class.

Name  Description  

Covariance 
Gets the variancecovariance matrix 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.
 
Variance 
Gets the variance 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 probability density function (pdf) 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(Random) 
Generates a random observation from the current distribution.
 
Generate(Double, Random) 
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.
 
Generate(Int32, Random) 
Generates a random vector of observations from the current distribution.
 
Generate(Int32, Random) 
Generates a random observation from the current distribution.
 
Generate(Int32, Double, Random) 
Generates a random vector of observations from the current distribution.
 
Generate(Int32, Int32, Random) 
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.)  
InnerComplementaryDistributionFunction 
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point x.
This function is also known as the Survival function.
 
InnerDistributionFunction 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
 
InnerLogProbabilityDensityFunction 
Gets the logprobability density function (pdf)
for this distribution evaluated at point x.
 
InnerProbabilityDensityFunction 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
 
LogProbabilityDensityFunction 
Gets the logprobability density function (pdf)
for this distribution evaluated at point x.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ProbabilityDensityFunction 
Gets the probability density function (pdf) 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.)  
To(Type)  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 ExtensionMethods.) 
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 value will occur is called the probability function (or probability density function, abbreviated PDF), 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).
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