LogarithmicSeriesDistribution Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents the logarithmic series distribution.

Inheritance Hierarchy

SystemObject
  Extreme.Statistics.DistributionsDistribution
    Extreme.Statistics.DistributionsDiscreteDistribution
      Extreme.Statistics.DistributionsLogarithmicSeriesDistribution

Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

Syntax

C++

Copy

[SerializableAttribute]
public class LogarithmicSeriesDistribution : DiscreteDistribution

<SerializableAttribute>
Public Class LogarithmicSeriesDistribution
	Inherits DiscreteDistribution

[SerializableAttribute]
public ref class LogarithmicSeriesDistribution : public DiscreteDistribution

[<SerializableAttribute>]
type LogarithmicSeriesDistribution =  
    class
        inherit DiscreteDistribution
    end

The LogarithmicSeriesDistribution type exposes the following members.

Constructors

	Name	Description
	LogarithmicSeriesDistribution(Double)	Constructs a new LogarithmicSeriesDistribution with the specified probability of success. The distribution uses the default random number generator to generate samples.
	LogarithmicSeriesDistribution(VectorDouble)	Estimates the parameter of the distribution of a variable assuming it follows a logarithmic series distribution.

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Properties

	Name	Description
	Entropy	Gets the entropy of the distribution. (Inherited from Distribution.)
	IsUnimodal	Gets whether the distribution has one or more modes. (Inherited from DiscreteDistribution.)
	Kurtosis	Gets the kurtosis of the distribution. (Overrides DistributionKurtosis.)
	Mean	Gets the mean or expectation value of the distribution. (Overrides DistributionMean.)
	Mode	Gets the mode of the distribution. (Overrides DiscreteDistributionMode.)
	NumberOfModes	Gets the number of modes of the distribution. (Inherited from DiscreteDistribution.)
	ProbabilityOfSuccess	Gets the probability that a trial is successful.
	Skewness	Gets the skewness of the distribution. (Overrides DistributionSkewness.)
	StandardDeviation	Gets the standard deviation of the distribution. (Inherited from Distribution.)
	StatisticSymbol	Gets the common symbol to describe a statistic from the distribution. (Inherited from Distribution.)
	Variance	Gets the variance of the distribution. (Overrides DistributionVariance.)

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Methods

	Name	Description
	DistributionFunction	Evaluates the cumulative distribution function of the distribution. (Overrides DiscreteDistributionDistributionFunction(Int32).)
	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.)
	GetAllModes	Returns an array that contains all the modes of the distribution. (Inherited from DiscreteDistribution.)
	GetExpectedHistogram(IndexIntervalInt32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples. (Inherited from DiscreteDistribution.)
	GetExpectedHistogram(IndexInt32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples. (Inherited from DiscreteDistribution.)
	GetExpectedHistogram(Int32, Int32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples. (Inherited from DiscreteDistribution.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetRandomSequence	Returns a sequence of random samples from the distribution. (Inherited from DiscreteDistribution.)
	GetRandomSequence(Random)	Returns a sequence of random samples from the distribution. (Inherited from DiscreteDistribution.)
	GetRandomSequence(Random, Int32)	Returns a sequence of random samples of the specified length from the distribution. (Inherited from DiscreteDistribution.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InverseDistributionFunction	Returns the inverse of the distribution function. (Overrides DiscreteDistributionInverseDistributionFunction(Double).)
	LeftTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound. (Inherited from DiscreteDistribution.)
	LogProbability	Returns the logarithm of the probability of obtaining a specific integer value in the distribution. (Inherited from DiscreteDistribution.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Probability(Int32)	Evaluates the probability function of the distribution. (Overrides DiscreteDistributionProbability(Int32).)
	Probability(Int32, Int32)	Gets the probability of obtaining a sample that falls within the specified interval from the distribution. (Inherited from DiscreteDistribution.)
	RightTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound. (Inherited from DiscreteDistribution.)
	Sample	Returns a random sample from the distribution. (Inherited from DiscreteDistribution.)
	Sample(Int32)	Returns a vector of random samples from the distribution. (Inherited from DiscreteDistribution.)
	Sample(Random)	Returns a random sample from the distribution. (Inherited from DiscreteDistribution.)
	Sample(Int32, Random)	Returns a vector of random samples from the distribution. (Inherited from DiscreteDistribution.)
	Sample(Random, Int32)	Fills an Int32 array with random numbers. (Inherited from DiscreteDistribution.)
	Sample(Random, Double)	Returns a single random sample from a logarithmic series distribution with the specified probability of success.
	Sample(Random, Int32, Int32, Int32)	Fills an Int32 array with random numbers from this DiscreteDistribution. (Inherited from DiscreteDistribution.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	TwoTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound. (Inherited from DiscreteDistribution.)

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Remarks

The logarithmic series distribution characterizes the probability of the number of trials required to achieve the first success, each trial having a probability probability of being successful.

The logarithmic series distribution is the discrete form of the Exponential distribution.

Reference

Extreme.Statistics.Distributions Namespace

Extreme.Statistics.DistributionsExponentialDistribution

Extreme.Statistics.DistributionsNegativeBinomialDistribution

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