Modifier and Type | Field and Description |
---|---|
protected Distribution |
SamplingDistributionExperiment.dist |
Modifier and Type | Method and Description |
---|---|
void |
ConfidenceControlPanel.makeObservable(Distribution dist) |
Modifier and Type | Field and Description |
---|---|
protected Distribution |
SOCRDistributions.dist |
protected Distribution |
SOCRDistributionFunctors.dist |
protected Distribution |
GraphPanels.dist |
Modifier and Type | Method and Description |
---|---|
static Distribution |
Distribution.getInstance(java.lang.String classname) |
Modifier and Type | Method and Description |
---|---|
void |
GraphPanel.setDistribution(Distribution d) |
void |
GraphPanels.setDistribution(Distribution d) |
void |
DistributionGraphPanel.setDistribution(Distribution d) |
void |
MGFGraphPanel.setDistribution(Distribution d) |
void |
PGFGraphPanel.setDistribution(Distribution d) |
Modifier and Type | Class and Description |
---|---|
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, Fréchet 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,…, p_r).
|
class |
NormalDistribution
This class encapsulates the normal distribution with specified (mean, SD)
parameters.
|
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 .
|
Modifier and Type | Method and Description |
---|---|
Distribution |
Convolution.getDistribution()
This method returns the distribution.
|
Distribution |
RandomVariable.getDistribution()
Get the probability distribution
|
Distribution[] |
MixtureDistribution.getDistributions()
This method returns the array of distributions.
|
Distribution |
MixtureDistribution.getDistributions(int i)
This method returns a particular distribution.
|
Modifier and Type | Method and Description |
---|---|
void |
Convolution.setDistribution(Distribution d)
This method sets the distribution.
|
void |
RandomVariable.setDistribution(Distribution d)
Assign the probability distribution and create a corresponding data
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
|
Constructor and Description |
---|
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
|
Modifier and Type | Field and Description |
---|---|
protected Distribution |
ConfidenceCanvasGeneralBase.chosenDistribution |
Modifier and Type | Method and Description |
---|---|
Distribution |
ConfidenceControlPanelGeneral.getDistribution() |
Modifier and Type | Method and Description |
---|---|
void |
ConfidenceCanvasGeneral.clear(int n,
int nTrials,
Distribution dist) |
void |
ConfidenceCanvasGeneralBase.clear(int n,
int nTrials,
Distribution dist)
clear clears canvas and resets parameters
|
void |
ConfidenceControlPanelGeneral.makeObservable(Distribution dist) |
void |
ConfidenceCanvasGeneral.setDistribution(Distribution dist) |
void |
ConfidenceCanvasGeneralBase.setDistribution(Distribution dist) |
void |
IntervalType.updateIntervalType(Distribution distribution,
int nTrials,
int sampleSize,
int cvIndex) |
Modifier and Type | Method and Description |
---|---|
Distribution |
QuantileGraph.getDistribution()
This method returns the distribution.
|
Modifier and Type | Method and Description |
---|---|
void |
QuantileGraph.setDistribution(Distribution d)
This method sets the distribution.
|
Constructor and Description |
---|
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.
|