Uses of Class
edu.uah.math.distributions.Distribution

Packages that use Distribution

edu.uah.math.devices
edu.uah.math.distributions

Uses of Distribution in edu.uah.math.devices

Methods in edu.uah.math.devices that return Distribution

Distribution	QuantileTable.getDistribution() This method returns the distribution
Distribution	DistributionGraph.getDistribution() This method returns the distribution associated with the graph.

Methods in edu.uah.math.devices with parameters of type Distribution

void	QuantileTable.setDistribution(Distribution d) This method sets the distribution.
void	DistributionGraph.setDistribution(Distribution d) This method specifies the distribution and sets up graph paramters.

Constructors in edu.uah.math.devices with parameters of type Distribution

CriticalGraph(Distribution d)
This general constructor creates a new critical graph with a specified distribution.

DistributionGraph(Distribution d)
This general constructor creates a new distribution graph with a specified distribution.

QuantileGraph(Distribution d)
This special constructor creates a new quantile graph that shows a specified distribution and the median (quanitle of order 0.5).

QuantileGraph(Distribution d, double x)
This general constructor creates a new quantile graph that shows a specified distribution and quanitle.

QuantileTable(Distribution d, double[] p)
This general constructor creates a new quantile table corresponding to a given distribution and a given array of probabilities.

QuantileTable(Distribution d, int n)
This general constructor creates a new quantile table corresponding to a given distribution and a uniform set of probabilities.

Uses of Distribution in edu.uah.math.distributions

Subclasses of Distribution in edu.uah.math.distributions

class	BernoulliDistribution This class models the Bernoulli distribution with a specified parameter.
class	BetaDistribution This class is models the beta distribution with specified left and right parameters.
class	BinomialDistribution This class models the binomial distribution with a specified number of trials and probability of success.
class	BinomialRandomNDistribution This class models the binomial distribution with a random number of trials.
class	BirthdayDistribution This class models the distribution of the number of distinct sample values when a sample of a specified size is chosen with replacement from a finite population of a specified size.
class	CauchyDistribution This class models the Cauchy distribution.
class	ChiSquareDistribution This class defines the chi-square distribution with a specifed degrees of freedom parameter.
class	CircleDistribution This class models the crcle distribution with a specified radius.
class	ContinuousUniformDistribution This class models the uniform distribution on a specified interval.
class	ConvolutionDistribution This class creates covolution of a given distribution to a given power.
class	CouponDistribution This class models the distribution of the sample size needed to get a specified number of distinct sample values when sampling with replacement from a finite population of a specified size.
class	DieDistribution This class models the distribution for a standard 6-sided die.
class	DiscreteArcsineDistribution This class models the discrete arcsine distribution.
class	DiscreteUniformDistribution This class models the discrete uniform distribution on a finite set.
class	ExponentialDistribution This class defines the standard exponential distribution with a specified rate parameter.
class	ExtremeValueDistribution This class models the exponential-type extreme value distribution.
class	FiniteDistribution This class models a basic discrete distribution on a finite set of points, with specified probabilities.
class	FiniteOrderStatisticDistribution This class models the distribution of an order statistic for a sample chosen without replacement from {1, 2..., N} .
class	FisherDistribution This class models the Fisher F distribution with a spcified number of degrees of freedom in the numerator and denominator.
class	GammaDistribution This class models the gamma distribution with a specified shape parameter and scale parameter.
class	GeometricDistribution This class models the geometric distribution with a given success probability.
class	HypergeometricDistribution This class models the hypergeometric distribution with a specified population size, sample size, and number of type 1 objects.
class	LocationScaleDistribution This class applies a location-scale tranformation to a given distribution.
class	LogisticDistribution This class models the logistic distribution.
class	LogNormalDistribution This class models the lognormal distribution with specified parameters.
class	MatchDistribution This class models the distribution of the number of matches in a random permutation.
class	MixtureDistribution This class models a distributions which is the mixture of a given set of distributions using a given set of probabilities as the mixing parameters
class	NegativeBinomialDistribution This class models the negative binomial distribution with specified successes parameter and probability parameter.
class	NormalDistribution This class encapsulates the normal distribution with specified parameters.
class	OrderStatisticDistribution This class models the distribution of the order statistic of a specified order from a random sample of a specified size from a specified sampling distribution.
class	ParetoDistribution This class models the Pareto distribution with a specified parameter.
class	PoissonDistribution The class models the Poisson distribution with a specified rate parameter.
class	StudentDistribution This class models the student t distribution with a specifed degrees of freeom parameter.
class	TriangleDistribution This class models the triangle distribution on a specified interval.
class	WalkMaxDistribution This class models the distribution of the maximum value of a symmetric random walk on the interval [0, n].
class	WalkPositionDistribution This class models the distribution of the position at time n for a random walk on the interval [0, n].
class	WeibullDistribution This class models the Weibull distribution with specified shape and scale parameters.

Methods in edu.uah.math.distributions that return Distribution

Distribution	RandomVariable.getDistribution() This method gets the probability distribution.
Distribution	OrderStatisticDistribution.getDistribution() This method returns the sampling distribution.
Distribution	LocationScaleDistribution.getDistribution() This method gets the underlying distribution that is being moved and scaled.
Distribution	ConvolutionDistribution.getDistribution() This method returns the distribution.
Distribution	BinomialRandomNDistribution.getDistribution() This method gets the distribution for the number of trials.
Distribution[]	MixtureDistribution.getDistributions() This method returns the array of distributions.
Distribution	MixtureDistribution.getDistributions(int i) This method returns a particular distribution.

Methods in edu.uah.math.distributions with parameters of type Distribution

void	RandomVariable.setDistribution(Distribution d) This method assigns the probability distribution and create a corresponding interval data distribution.
void	OrderStatisticDistribution.setDistribution(Distribution d) This method sets the sampling distribution.
void	LocationScaleDistribution.setDistribution(Distribution d) This method sets the distribution to be moved and scaled.
void	ConvolutionDistribution.setDistribution(Distribution d) This method sets the distribution.
void	BinomialRandomNDistribution.setDistribution(Distribution d) This method sets the distribution for the number of trials.
void	MixtureDistribution.setDistributions(Distribution[] d) This method sets the distributions.
void	MixtureDistribution.setDistributions(int i, Distribution d) This method sets a particular distribution.
void	MixtureDistribution.setParameters(Distribution[] d, double[] p) This method sets up the domain of the general mixture distributions in terms of the distributions being mixed.
void	MixtureDistribution.setParameters(Distribution d0, Distribution d1, double a) This method sets up the domain of for the mixture of two distributions.
void	BinomialRandomNDistribution.setParameters(Distribution d, double p) This method sets the parameters: the distribution for the number of trials and the probability of success.
void	LocationScaleDistribution.setParameters(Distribution d, double a, double b) This method sets the parameters, the distribution and the location and scale parameters, and sets up the domain.
void	ConvolutionDistribution.setParameters(Distribution d, int n) This method sets the parameters: the distribution and convolution power.
void	OrderStatisticDistribution.setParameters(Distribution d, int n, int k) This method sets the parameters: the sampling distribution, sample size, and order.

Constructors in edu.uah.math.distributions with parameters of type Distribution

BinomialRandomNDistribution(Distribution d, double p)
This general constructor creates a new randomized binomial distribution with a specified probability of success and a specified distribution for the number of trials.

ConvolutionDistribution(Distribution d, int n)
This general constructor: creates a new convolution distribution corresponding to a specified distribution and convolution power.

LocationScaleDistribution(Distribution d, double a, double b)
This general constructor creates a new location-scale transformation on a given distribuiton with given location and scale parameters.

MixtureDistribution(Distribution[] d, double[] p)
This general constructor creates the mixture of a given array of distribuitons using a given array of probabilities as the mixing parameters.

MixtureDistribution(Distribution d0, Distribution d1)
This special constructor creates the mixture of two distributions with equal mixing probabilities

MixtureDistribution(Distribution d0, Distribution d1, double a)
This special constructor creates the mixture of two distributions using a specified number and its complement as the mixing probabilities.

OrderStatisticDistribution(Distribution d, int n, int k)
This general constructor creates a new order statistic distribution corresponding to a specified sampling distribution, sample size, and order.

RandomVariable(Distribution d)
This special constructor creates a new random variable with a specified probability distribution and the default name "X".

RandomVariable(Distribution d, java.lang.String n)
This general constructor creates a new random variable with a specified probability distribution and name.