Package | Description |
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edu.uah.math.devices | |
edu.uah.math.distributions |
Modifier and Type | Method and Description |
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Distribution |
DistributionGraph.getDistribution()
This method returns the distribution associated with the graph.
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Distribution |
QuantileTable.getDistribution()
This method returns the distribution
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Modifier and Type | Method and Description |
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void |
DistributionGraph.setDistribution(Distribution d)
This method specifies the distribution and sets up graph paramters.
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void |
QuantileTable.setDistribution(Distribution d)
This method sets the distribution.
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Constructor and Description |
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CriticalGraph(Distribution d)
This general constructor creates a new critical graph with a specified distribution.
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DistributionGraph(Distribution d)
This general constructor creates a new distribution graph with a
specified distribution.
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QuantileGraph(Distribution d)
This special constructor creates a new quantile graph that shows a specified
distribution and the median (quanitle of order 0.5).
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QuantileGraph(Distribution d,
double x)
This general constructor creates a new quantile graph that shows a specified
distribution and quanitle.
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QuantileTable(Distribution d,
double[] p)
This general constructor creates a new quantile table corresponding to a
given distribution and a given array of probabilities.
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QuantileTable(Distribution d,
int n)
This general constructor creates a new quantile table corresponding to a
given distribution and a uniform set of probabilities.
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Modifier and Type | Class and Description |
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class |
BernoulliDistribution
This class models the Bernoulli distribution with a specified parameter.
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class |
BetaDistribution
This class is models the beta distribution with specified left and right parameters.
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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.
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class |
CauchyDistribution
This class models the Cauchy distribution.
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class |
ChiSquareDistribution
This class defines the chi-square distribution with a specifed degrees of
freedom parameter.
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class |
CircleDistribution
This class models the crcle distribution with a specified radius.
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class |
ContinuousUniformDistribution
This class models the uniform distribution on a specified interval.
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class |
ConvolutionDistribution
This class creates covolution of a given distribution to a given power.
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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.
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class |
DiscreteUniformDistribution
This class models the discrete uniform distribution on a finite set.
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class |
ExponentialDistribution
This class defines the standard exponential distribution with a specified rate parameter.
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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.
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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.
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class |
LocationScaleDistribution
This class applies a location-scale tranformation to a given distribution.
|
class |
LogisticDistribution
This class models the logistic distribution.
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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.
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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.
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class |
StudentDistribution
This class models the student t distribution with a specifed degrees of freeom
parameter.
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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].
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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.
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Modifier and Type | Method and Description |
---|---|
Distribution |
RandomVariable.getDistribution()
This method gets the probability distribution.
|
Distribution |
BinomialRandomNDistribution.getDistribution()
This method gets the distribution for the number of trials.
|
Distribution |
ConvolutionDistribution.getDistribution()
This method returns the distribution.
|
Distribution |
LocationScaleDistribution.getDistribution()
This method gets the underlying distribution that is being moved and scaled.
|
Distribution |
OrderStatisticDistribution.getDistribution()
This method returns the sampling distribution.
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Distribution[] |
MixtureDistribution.getDistributions()
This method returns the array of distributions.
|
Distribution |
MixtureDistribution.getDistributions(int i)
This method returns a particular distribution.
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Modifier and Type | Method and Description |
---|---|
void |
RandomVariable.setDistribution(Distribution d)
This method assigns the probability distribution and create a corresponding
interval data distribution.
|
void |
BinomialRandomNDistribution.setDistribution(Distribution d)
This method sets the distribution for the number of trials.
|
void |
ConvolutionDistribution.setDistribution(Distribution d)
This method sets the distribution.
|
void |
LocationScaleDistribution.setDistribution(Distribution d)
This method sets the distribution to be moved and scaled.
|
void |
OrderStatisticDistribution.setDistribution(Distribution d)
This method sets the sampling 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.
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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.
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Constructor and Description |
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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.
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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
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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.
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RandomVariable(Distribution d)
This special constructor creates a new random variable with a specified
probability distribution and the default name "X".
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RandomVariable(Distribution d,
java.lang.String n)
This general constructor creates a new random variable with a specified
probability distribution and name.
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