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

UniformDiscreteDistribution 
Creates a discrete uniform distribution defined in the interval [a;b].

Name  Description  

Entropy 
Gets the entropy for this distribution.
(Overrides UnivariateDiscreteDistributionEntropy.)  
Length 
Gets the length of the distribution (b  a + 1).
 
Maximum 
Gets the maximum value of the distribution (b).
 
Mean 
Gets the mean for this distribution.
(Overrides UnivariateDiscreteDistributionMean.)  
Median 
Gets the median for this distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Minimum 
Gets the minimum value of the distribution (a).
 
Mode 
Gets the mode for this distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Quartiles 
Gets the Quartiles for this distribution.
(Inherited from UnivariateDiscreteDistribution.)  
StandardDeviation 
Gets the Standard Deviation (the square root of
the variance) for the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Support 
Gets the support interval for this distribution.
(Overrides UnivariateDiscreteDistributionSupport.)  
Variance 
Gets the variance for this distribution.
(Overrides UnivariateDiscreteDistributionVariance.) 
Name  Description  

BaseDistributionFunction 
Computes the cumulative distribution function by summing the outputs of the ProbabilityMassFunction(Int32)
for all elements in the distribution domain. Note that this method should not be used in case there is a more
efficient formula for computing the CDF of a distribution.
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
Clone 
Creates a new object that is a copy of the current instance.
(Overrides DistributionBaseClone.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
CumulativeHazardFunction 
Gets the cumulative hazard function for this
distribution evaluated at point x.
(Inherited from UnivariateDiscreteDistribution.)  
DistributionFunction(Int32) 
Gets P(X ≤ k), the cumulative distribution function
(cdf) for this distribution evaluated at point k.
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
DistributionFunction(Int32, Int32) 
Gets the cumulative distribution function (cdf) for this
distribution in the semiclosed interval (a; b] given as
P(a < X ≤ b).
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Int32) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Double, Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Double, Int32) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Double, Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Overrides UnivariateDiscreteDistributionFit(Double, Double, IFittingOptions).)  
Fit(Double, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Int32, Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Fit(Int32, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from UnivariateDiscreteDistribution.)  
Generate 
Generates a random observation from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Generate(Random) 
Generates a random observation from the current distribution.
(Overrides UnivariateDiscreteDistributionGenerate(Random).)  
Generate(Int32) 
Generates a random vector of observations from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Generate(Int32, Double) 
Generates a random vector of observations from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Generate(Int32, Int32) 
Generates a random vector of observations from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Generate(Int32, Random) 
Generates a random vector of observations from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
Generate(Int32, Int32, Random) 
Generates a random vector of observations from the current distribution.
(Overrides UnivariateDiscreteDistributionGenerate(Int32, Int32, Random).)  
Generate(Int32, Double, Random) 
Generates a random vector of observations from the current distribution.
(Inherited from UnivariateDiscreteDistribution.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetRange 
Gets the distribution range within a given percentile.
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
InnerComplementaryDistributionFunction 
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.
(Inherited from UnivariateDiscreteDistribution.)  
InnerDistributionFunction 
Gets the cumulative distribution function (cdf) for
this distribution evaluated at point k.
(Overrides UnivariateDiscreteDistributionInnerDistributionFunction(Int32).)  
InnerInverseDistributionFunction 
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.
(Inherited from UnivariateDiscreteDistribution.)  
InnerLogProbabilityMassFunction 
Gets the logprobability mass function (pmf) for
this distribution evaluated at point x.
(Overrides UnivariateDiscreteDistributionInnerLogProbabilityMassFunction(Int32).)  
InnerProbabilityMassFunction 
Gets the probability mass function (pmf) for
this distribution evaluated at point x.
(Overrides UnivariateDiscreteDistributionInnerProbabilityMassFunction(Int32).)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
LogCumulativeHazardFunction 
Gets the logcumulative hazard function for this
distribution evaluated at point x.
(Inherited from UnivariateDiscreteDistribution.)  
LogProbabilityMassFunction 
Gets the logprobability mass function (pmf) for
this distribution evaluated at point x.
(Inherited from UnivariateDiscreteDistribution.)  
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.
(Inherited from UnivariateDiscreteDistribution.)  
QuantileDensityFunction 
Gets the first derivative of the
inverse distribution function (icdf) for this distribution evaluated
at probability p.
(Inherited from UnivariateDiscreteDistribution.)  
Random 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Int32) 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Random) 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Int32, Int32) 
Generates a random observation from the
Uniform distribution with the given parameters.
 
Random(Int32, Int32) 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Int32, Random) 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Int32, Int32, Int32) 
Generates a random vector of observations from the
Uniform distribution with the given parameters.
 
Random(Int32, Int32, Random) 
Generates a random observation from the
Uniform distribution with the given parameters.
 
Random(Int32, Int32, Random) 
Generates a random observation from the Uniform
distribution defined in the interval 0 and MAXVALUE.
 
Random(Int32, Int32, Int32, Int32) 
Generates a random vector of observations from the
Uniform distribution with the given parameters.
 
Random(Int32, Int32, Int32, Random) 
Generates a random vector of observations from the
Uniform distribution with the given parameters.
 
Random(Int32, Int32, Int32, Int32, Random) 
Generates a random vector of observations from the
Uniform distribution with the given parameters.
 
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.
(Overrides DistributionBaseToString(String, IFormatProvider).) 
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 
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.) 
In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution whereby a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n. Another way of saying "discrete uniform distribution" would be "a known, finite number of outcomes equally likely to happen".
A simple example of the discrete uniform distribution is throwing a fair die. The possible values are 1, 2, 3, 4, 5, 6, and each time the die is thrown the probability of a given score is 1/6. If two dice are thrown and their values added, the resulting distribution is no longer uniform since not all sums have equal probability.
The discrete uniform distribution itself is inherently nonparametric. It is convenient, however, to represent its values generally by an integer interval [a,b], so that a,b become the main parameters of the distribution (often one simply considers the interval [1,n] with the single parameter n).
References:
// Create an uniform (discrete) distribution in [2, 6] var dist = new UniformDiscreteDistribution(a: 2, b: 6); // Common measures double mean = dist.Mean; // 4.0 double median = dist.Median; // 4.0 double var = dist.Variance; // 1.3333333333333333 // Cumulative distribution functions double cdf = dist.DistributionFunction(k: 2); // 0.2 double ccdf = dist.ComplementaryDistributionFunction(k: 2); // 0.8 // Probability mass functions double pmf1 = dist.ProbabilityMassFunction(k: 4); // 0.2 double pmf2 = dist.ProbabilityMassFunction(k: 5); // 0.2 double pmf3 = dist.ProbabilityMassFunction(k: 6); // 0.2 double lpmf = dist.LogProbabilityMassFunction(k: 2); // 1.6094379124341003 // Quantile function int icdf1 = dist.InverseDistributionFunction(p: 0.17); // 2 int icdf2 = dist.InverseDistributionFunction(p: 0.46); // 4 int icdf3 = dist.InverseDistributionFunction(p: 0.87); // 6 // Hazard (failure rate) functions double hf = dist.HazardFunction(x: 4); // 0.5 double chf = dist.CumulativeHazardFunction(x: 4); // 0.916290731874155 // String representation string str = dist.ToString(CultureInfo.InvariantCulture); // "U(x; a = 2, b = 6)"