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

UnivariateDiscreteDistribution 
Constructs a new UnivariateDistribution class.

Name  Description  

Entropy 
Gets the entropy for this distribution.
 
Mean 
Gets the mean for this distribution.
 
Median 
Gets the median for this distribution.
 
Mode 
Gets the mode for this distribution.
 
Quartiles 
Gets the Quartiles for this distribution.
 
StandardDeviation 
Gets the Standard Deviation (the square root of
the variance) for the current distribution.
 
Support 
Gets the support interval for this distribution.
 
Variance 
Gets the variance for this distribution.

Name  Description  

BaseInverseDistributionFunction 
Gets the inverse of the cumulative distribution function (icdf) for
this distribution evaluated at probability p using a numerical
approximation based on binary search.
 
Clone 
Creates a new object that is a copy of the current instance.
(Inherited from DistributionBase.)  
ComplementaryDistributionFunction(Int32) 
Gets P(X > k) the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point k.
This function is also known as the Survival function.
 
ComplementaryDistributionFunction(Int32, Boolean) 
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point k.
This function is also known as the Survival function.
 
CumulativeHazardFunction 
Gets the cumulative hazard function for this
distribution evaluated at point x.
 
DistributionFunction(Int32) 
Gets P(X ≤ k), the cumulative distribution function
(cdf) for this distribution evaluated at point k.
 
DistributionFunction(Int32, Boolean) 
Gets P(X ≤ k) or P(X < k), the cumulative distribution function
(cdf) for this distribution evaluated at point k, depending
on the value of the inclusive parameter.
 
DistributionFunction(Int32, Int32) 
Gets the cumulative distribution function (cdf) for this
distribution in the semiclosed interval (a; b] given as
P(a < X ≤ b).
 
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(Int32) 
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(Int32, IFittingOptions) 
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.
 
Fit(Int32, Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
 
Fit(Int32, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
 
Generate 
Generates a random observation from the current distribution.
 
Generate(Int32) 
Generates a random vector of observations 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.)  
GetRange 
Gets the distribution range within a given percentile.
 
GetType  Gets the Type of the current instance. (Inherited from Object.)  
HazardFunction 
Gets the hazard function, also known as the failure rate or
the conditional failure density function for this distribution
evaluated at point x.
 
InverseDistributionFunction 
Gets the inverse of the cumulative distribution function (icdf) for
this distribution evaluated at probability p. This function
is also known as the Quantile function.
 
LogCumulativeHazardFunction 
Gets the logcumulative hazard function for this
distribution evaluated at point x.
 
LogProbabilityMassFunction 
Gets the logprobability mass function (pmf) for
this distribution evaluated at point x.
 
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.
 
QuantileDensityFunction 
Gets the first derivative of the
inverse distribution function (icdf) for this distribution evaluated
at probability p.
 
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.)  
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).
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