Uses of Class
edu.ucla.stat.SOCR.core.Distribution

Packages that use Distribution

edu.uah.math.experiments
edu.ucla.stat.SOCR.analyses.util
edu.ucla.stat.SOCR.core
edu.ucla.stat.SOCR.distributions
edu.ucla.stat.SOCR.experiments.util
edu.ucla.stat.SOCR.util

Uses of Distribution in edu.uah.math.experiments

Fields in edu.uah.math.experiments declared as Distribution

protected Distribution

SamplingDistributionExperiment.dist

Uses of Distribution in edu.ucla.stat.SOCR.analyses.util

Methods in edu.ucla.stat.SOCR.analyses.util with parameters of type Distribution

void	ConfidenceControlPanel.makeObservable(Distribution dist)

Uses of Distribution in edu.ucla.stat.SOCR.core

Fields in edu.ucla.stat.SOCR.core declared as Distribution

protected Distribution	SOCRDistributions.dist
protected Distribution	SOCRDistributionFunctors.dist
protected Distribution	GraphPanels.dist

Methods in edu.ucla.stat.SOCR.core that return Distribution

static Distribution

Distribution.getInstance(java.lang.String classname)

Methods in edu.ucla.stat.SOCR.core with parameters of type Distribution

void	PGFGraphPanel.setDistribution(Distribution d)
void	MGFGraphPanel.setDistribution(Distribution d)
void	GraphPanels.setDistribution(Distribution d)
void	GraphPanel.setDistribution(Distribution d)
void	DistributionGraphPanel.setDistribution(Distribution d)

Uses of Distribution in edu.ucla.stat.SOCR.distributions

Subclasses of Distribution in edu.ucla.stat.SOCR.distributions

class	AndersonDarlingDistribution This class defines the Anderson-Darling distribution with a specifed parameter n>=1.
class	ArcSineDistribution This class models the Arc-Sine distribution on a specified interval.
class	BenfordDistribution This class models the Benford distribution with parameters m (population size), n (sample size), and r (number of type 1 objects).
class	BernoulliDistribution The Bernoulli distribution with parameter p http://mathworld.wolfram.com/BernoulliDistribution.html .
class	BetaBinomialDistribution The binomial distribution with specified parameters: the number of trials (n) and the probability of success (p) http://mathworld.wolfram.com/BetaBinomialDistribution.html .
class	BetaDistribution A Java implmentation of the beta distribution with specified left(alpha) and right(beta) parameters http://mathworld.wolfram.com/BetaDistribution.html .
class	BetaGeneralDistribution A Java implmentation of the (General) Beta Distribution with specified: left(alpha) and right(beta) parameters AND LIMITS A and B.
class	BinomialDistribution The binomial distribution with specified parameters: the number of trials (n) and the probability of success (p) http://mathworld.wolfram.com/BinomialDistribution.html .
class	BinomialRandomNDistribution 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	ChiDistribution This class defines the Chi distribution with a specifed degrees of freedom.
class	ChiSquareDistribution This class defines the chi-square distribution with a specifed degrees of freedom.
class	CircleDistribution This class models the Circle distribution with parameter a (radius).
class	ContinuousUniformDistribution This class models the uniform distribution on a specified interval.
class	Convolution This class creates the n-fold convolution of a given distribution
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: http://www.math.uah.edu/stat/urn/Coupon.xhtml
class	DieDistribution Distribution for a standard 6-sided die
class	DiscreteArcsineDistribution This class models the discrete ArcSine distribution that governs the last zero in a symmetric random walk on an interval.
class	DiscreteUniformDistribution The discrete uniform distribution on a finite set.
class	ErlangDistribution A Java implmentation of the Erlang distribution with specified Scale (scale) and shape (shape) parameters http://mathworld.wolfram.com/ErlangDistribution.html .
class	ErrorDistribution A Java implementation of the Error distribution with specified Location, Scale and Shape parameters http://en.wikipedia.org/wiki/Exponential_power_distribution.
class	ExponentialDistribution This class defines the (general) Exponential distribution with rate parameter r and shift parameter s.
class	FiniteDistribution A basic discrete distribution on a finite set of points, with specified probabilities
class	FiniteOrderStatisticDistribution This class models the distribution of the k'th order statistic for a sample of size n 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	FisherTippettDistribution A Java implmentation of the FisherTippettdistribution with specified alpha & beta parameters http://mathworld.wolfram.com/FisherTippettDistribution.html .
class	GammaDistribution Gamma distribution with a specified shape parameter and scale parameter.
class	GeneralCauchyDistribution A Java implmentation of the General Cauchy distribution with specified alpha & beta parameters.
class	GeneralizedExtremeValueDistribution This class models the Generalized-Extreme-Value (GEV) Distribution with specified 3 parameters (location, scale, shape): The generalized extreme value distribution (GEV) is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, FrEchet and Weibull families also known as type I, II and III extreme value distributions.
class	GeometricDistribution The geometric distribution with parameter p.
class	GilbratsDistribution This class models the Gilbrat's distribution: A continuous distribution in which the logarithm of a variable x has a Standard Normal distribution.
class	GompertzDistribution Gompertz distribution with a specified shape parameter and scale parameter.
class	GumbelDistribution A Java implmentation of the Gumbel distribution with specified alpha & beta parameters http://mathworld.wolfram.com/GumbelDistribution.html .
class	HalfNormalDistribution This class models the Half-Normal distribution with specified starting and SD parameters.
class	HyperbolicSecantDistribution This class encapsulates the Hyperbolic-Secant distribution -- no parameters.
class	HypergeometricDistribution This class models the HyperGeometric distribution with parameters m (population size), n (sample size), and r (number of type 1 objects).
class	InverseGammaDistribution Gamma distribution with a specified shape parameter and scale parameter.
class	InverseGaussianDistribution This class encapsulates the normal distribution with specified (mean, SD) parameters.
class	JohnsonSBDistribution This class models the Johnson SB (Special Bounded) distribution with specified first 4 parameters (mean, SD, skewness, kurtosis): The Johnson family of distributions (N.L.
class	JohnsonSUDistribution This class models the Johnson SU (Special Unbounded) distribution with specified first 4 parameters (mean, SD, skewness, kurtosis): The Johnson family of distributions (N.L.
class	KolmogorovDistribution This class defines the Kolmogorov distribution with a specifed parameter n>=1.
class	LaplaceDistribution This class defines the Laplace distribution with parameters mu & beta.
class	LocationScaleDistribution This class applies a location-scale tranformation to a given distribution.
class	LogarithmicSeriesDistribution A Java implmentation of the LogarithmicSeries distribution with specified shape (shape) parameters http://en.wikipedia.org/wiki/Logarithmic_distribution .
class	LogisticDistribution This class models the logistic distribution
class	LogisticExponentialDistribution This class models the LogisticExponential distribution http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/lexpdf.htm
class	LogNormalDistribution This class models the lognormal distribution with specified (mean & SD) parameters.
class	LomaxDistribution This class models the Lomax distribution (Pareto-distribution of hte second-kind) with a specified parameters (alpha=shape1; gamma=shape2).
class	MatchDistribution The distribution of the number of matches in a random permutation
class	MaxwellDistribution This class models the Maxwell distribution with parameter a.
class	MinimaxDistribution A Java implmentation of the Minimax distribution with specified left(alpha) and right(Minimax) parameters http://mathworld.wolfram.com/topics/ContinuousDistributions.html .
class	MixtureDistribution The Mixture distribution with parameter-vector p=(p1, p2, ..., pn) http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture is the (linear) mixure of an array of distributions according to the mixing parameters.
class	MultiNomialDistribution The Multinomial distribution with parameter-vector (k,p), where n=number of trials and event-probabilities p=(p1, p2, ..., pn), with sum(p_k)=1 and p_k>=0, 1<=k<=n.
class	MuthDistribution A Java implmentation of the Muth distribution with specified left(alpha) and right(Muth) parameters http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/mutpdf.htm .
class	NegativeBinomialDistribution This class models the negative binomial distribution with specified successes parameter and probability parameter.
class	NegativeHypergeometricDistribution This class models the NegativeHypergeometric distribution with parameters B (population size), b (sample size), and w (number of special-type 1 objects).
class	NegativeMultiNomialDistribution The Negative-Multinomial distribution with parameter-vector (x_o,p), where gamma = x_o>=0, and p=(p_1,0, p_r).
class	NonCentralChiSquareDistribution This class encapsulates the Non-Central Chi-Square distribution with specified: k > 0\, degrees of freedom, and \lambda > 0, non-centrality parameters parameters.
class	NormalDistribution This class encapsulates the normal distribution with specified (mean, SD) parameters.
class	NormalTruncatedDistribution This class encapsulates the Truncated Normal distribution with specified parameters: (mean, SD, leftSupportLimit, rightSupportLimit) http://en.wikipedia.org/wiki/Truncated_normal_distribution .
class	OrderStatisticDistribution 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 parameters (alpha=power; theta=LeftStart).
class	PointMassDistribution Point mass at x0.
class	PoissonDistribution A Java implementation of the Poisson distribution with specified Shift and Mean parameters http://en.wikipedia.org/wiki/Poisson_distribution.
class	PokerDiceDistribution
class	PowerFunctionDistribution A Java implementation of the PowerFunction distribution with specified Location, Scale and Shape parameters http://www.mathwave.com/articles/power_function_distribution.html.
class	RayleighDistribution A Java implmentation of the Rayleigh distribution with specified alpha & beta parameters http://mathworld.wolfram.com/RayleighDistribution.html
class	RiceDistribution This class models the Rice (Rician) distribution.
class	StudentDistribution This class models the Student T distribution with a specifed degrees of freedom parameter.
class	TriangleDistribution This class models the Triangular distribution on a specified interval.
class	TwoSidedPowerDistribution A Java implementation of the (Two-Sided POwer (TSP) Distribution with specified: left, right, mean and power parameters http://www.springerlink.com/content/u71g0104356x70u1/ .
class	UQuadraticDistribution This class models the Quadratic U distribution on a specified interval.
class	VonMisesDistribution This class models the Von-Mises (Circular Gaussian) distribution on [-Pi; Pi].
class	WalkMaxDistribution This class models the distribution of the maximum value of a symmetric random walk on the interval [0, n].
class	WalkPositionDistribution
class	WeibullDistribution This class models the Weibull distribution with specified shape and scale parameters.
class	ZipfMandelbrotDistribution This class models the Zipf-Mandelbrot distribution with parameters N be the number of elements; k be their rank (the value of the random-variable!); w be the value of the power-exponent characterizing the distribution; q be the (rank-)shift [0, \infty) http://en.wikipedia.org/wiki/Zipf-Mandelbrot_law .

Methods in edu.ucla.stat.SOCR.distributions that return Distribution

Distribution	RandomVariable.getDistribution() Get the probability distribution
Distribution	Convolution.getDistribution() This method returns the distribution.
Distribution[]	MixtureDistribution.getDistributions() This method returns the array of distributions.
Distribution	MixtureDistribution.getDistributions(int i) This method returns a particular distribution.

Methods in edu.ucla.stat.SOCR.distributions with parameters of type Distribution

void	RandomVariable.setDistribution(Distribution d) Assign the probability distribution and create a corresponding data distribution
void	Convolution.setDistribution(Distribution d) This method sets the distribution.
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) Set 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
void	Convolution.setParameters(Distribution d, int n) This method sets the parameters: the distribution and convolution power.
void	OrderStatisticDistribution.setParameters(Distribution d, int n, int k) Set the parameters: the sampling distribution, sample size, and order

Constructors in edu.ucla.stat.SOCR.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

Convolution(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 distributitons 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)
General constructor: creates a new order statistic distribution corresponding to a specified sampling distribution, sample size, and order

RandomVariable(Distribution d)
Special constructor: create a new random variable with a specified probability distribution and the name X

RandomVariable(Distribution d, java.lang.String n)
General constructor: create a new random variable with a specified probability distribution and name

Uses of Distribution in edu.ucla.stat.SOCR.experiments.util

Fields in edu.ucla.stat.SOCR.experiments.util declared as Distribution

protected Distribution

ConfidenceCanvasGeneralBase.chosenDistribution

Methods in edu.ucla.stat.SOCR.experiments.util that return Distribution

Distribution

ConfidenceControlPanelGeneral.getDistribution()

Methods in edu.ucla.stat.SOCR.experiments.util with parameters of type Distribution

void	ConfidenceCanvasGeneralBase.clear(int n, int nTrials, Distribution dist) clear clears canvas and resets parameters
void	ConfidenceCanvasGeneral.clear(int n, int nTrials, Distribution dist)
void	SimulationResampleInferencePanel.makeObservable(Distribution dist)
void	ConfidenceControlPanelGeneral.makeObservable(Distribution dist)
void	ConfidenceCanvasGeneralBase.setDistribution(Distribution dist)
void	ConfidenceCanvasGeneral.setDistribution(Distribution dist)
void	IntervalType.updateIntervalType(Distribution distribution, int nTrials, int sampleSize, int cvIndex)

Uses of Distribution in edu.ucla.stat.SOCR.util

Methods in edu.ucla.stat.SOCR.util that return Distribution

Distribution

QuantileGraph.getDistribution() This method returns the distribution.

Methods in edu.ucla.stat.SOCR.util with parameters of type Distribution

void	QuantileGraph.setDistribution(Distribution d) This method sets the distribution.

Constructors in edu.ucla.stat.SOCR.util with parameters of type Distribution

QuantileGraph(Distribution distribution)
This special constructor creates a new getQuantile graph with a specified distribution and the median (quanitle of order 0.5).

QuantileGraph(Distribution distribution, double getQuantile)
This general constructor creates a new getQuantile graph with a specified distribution and getQuantile.