See: Description
Class | Description |
---|---|
BernoulliDistribution |
A Bernoulli distribution, which takes a value of "1" with probability "p"
and value of "0" with probability "1-p".
|
BernoulliDistribution.CDF |
CDF of a Bernoulli distribution.
|
BernoulliDistribution.PMF |
PMF of the Bernoulli distribution.
|
BetaBinomialDistribution |
A Binomial distribution where the binomial parameter, p, is set according
to a Beta distribution instead of a single value.
|
BetaBinomialDistribution.CDF |
CDF of BetaBinomialDistribution
|
BetaBinomialDistribution.MomentMatchingEstimator |
Estimates the parameters of a Beta-binomial distribution using the matching
of moments, not maximum likelihood.
|
BetaBinomialDistribution.PMF |
PMF of the BetaBinomialDistribution
|
BetaDistribution |
Computes the Beta-family of probability distributions.
|
BetaDistribution.CDF |
CDF of the Beta-family distribution
|
BetaDistribution.MomentMatchingEstimator |
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
|
BetaDistribution.PDF |
Beta distribution probability density function
|
BetaDistribution.WeightedMomentMatchingEstimator |
Estimates the parameters of a Beta distribution using the matching
of moments, not maximum likelihood.
|
BinomialDistribution |
Binomial distribution, which is a collection of Bernoulli trials
|
BinomialDistribution.CDF |
CDF of the Binomial distribution, which is the probability of getting
up to "x" successes in "N" trials with a Bernoulli probability of "p"
|
BinomialDistribution.MaximumLikelihoodEstimator |
Maximum likelihood estimator of the distribution
|
BinomialDistribution.PMF |
The Probability Mass Function of a binomial distribution.
|
CategoricalDistribution |
The Categorical Distribution is the multivariate generalization of the
Bernoulli distribution, where the outcome of an experiment is a one-of-N
output, where the output is a selector Vector.
|
CategoricalDistribution.PMF |
PMF of the Categorical Distribution
|
CauchyDistribution |
A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes
known as the Lorentz distribution.
|
CauchyDistribution.CDF |
CDF of the CauchyDistribution.
|
CauchyDistribution.PDF |
PDF of the CauchyDistribution.
|
ChineseRestaurantProcess |
A Chinese Restaurant Process is a discrete stochastic processes that
partitions data points to clusters.
|
ChineseRestaurantProcess.PMF |
PMF of the Chinese Restaurant Process
|
ChiSquareDistribution |
Describes a Chi-Square Distribution.
|
ChiSquareDistribution.CDF |
Cumulative Distribution Function (CDF) of a Chi-Square Distribution
|
ChiSquareDistribution.PDF |
PDF of the Chi-Square distribution
|
DataCountTreeSetBinnedMapHistogram<ValueType extends java.lang.Comparable<? super ValueType>> |
The
DataCountTreeSetBinnedMapHistogram class extends a
DefaultDataDistribution by mapping values to user defined bins
using a TreeSetBinner . |
DefaultDataDistribution<KeyType> |
A default implementation of
ScalarDataDistribution that uses a
backing map. |
DefaultDataDistribution.DefaultFactory<DataType> |
A factory for
DefaultDataDistribution objects using some given
initial capacity for them. |
DefaultDataDistribution.Estimator<KeyType> |
Estimator for a DefaultDataDistribution
|
DefaultDataDistribution.PMF<KeyType> |
PMF of the DefaultDataDistribution
|
DefaultDataDistribution.WeightedEstimator<KeyType> |
A weighted estimator for a DefaultDataDistribution
|
DeterministicDistribution |
A deterministic distribution that returns samples at a single point.
|
DeterministicDistribution.CDF |
CDF of the deterministic distribution.
|
DeterministicDistribution.PMF |
PMF of the deterministic distribution.
|
DirichletDistribution |
The Dirichlet distribution is the multivariate generalization of the beta
distribution.
|
DirichletDistribution.PDF |
PDF of the Dirichlet distribution.
|
ExponentialDistribution |
An Exponential distribution describes the time between events in a poisson
process, resulting in a memoryless distribution.
|
ExponentialDistribution.CDF |
CDF of the ExponentialDistribution.
|
ExponentialDistribution.MaximumLikelihoodEstimator |
Creates a ExponentialDistribution from data
|
ExponentialDistribution.PDF |
PDF of the ExponentialDistribution.
|
ExponentialDistribution.WeightedMaximumLikelihoodEstimator |
Creates a ExponentialDistribution from weighted data
|
GammaDistribution |
Class representing the Gamma distribution.
|
GammaDistribution.CDF |
CDF of the Gamma distribution
|
GammaDistribution.MomentMatchingEstimator |
Computes the parameters of a Gamma distribution by the
Method of Moments
|
GammaDistribution.PDF |
Closed-form PDF of the Gamma distribution
|
GammaDistribution.WeightedMomentMatchingEstimator |
Estimates the parameters of a Gamma distribution using the matching
of moments, not maximum likelihood.
|
GeometricDistribution |
The geometric distribution models the number of successes before the first
failure occurs under an independent succession of Bernoulli tests.
|
GeometricDistribution.CDF |
CDF of the Geometric distribution
|
GeometricDistribution.MaximumLikelihoodEstimator |
Maximum likelihood estimator of the distribution
|
GeometricDistribution.PMF |
PMF of the Geometric distribution
|
InverseGammaDistribution |
Defines an inverse-gamma distribution.
|
InverseGammaDistribution.CDF |
CDF of the inverseRootFinder-gamma distribution.
|
InverseGammaDistribution.PDF |
PDF of the inverseRootFinder-Gamma distribution.
|
InverseWishartDistribution |
The Inverse-Wishart distribution is the multivariate generalization of the
inverse-gamma distribution.
|
InverseWishartDistribution.MultivariateGammaFunction |
Multivariate generalization of the Gamma function.
|
InverseWishartDistribution.PDF |
PDF of the Inverse-Wishart distribution, though I have absolutely no
idea why anybody would evaluate the PDF of an Inverse-Wishart...
|
KolmogorovDistribution |
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
KolmogorovDistribution.CDF |
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
LaplaceDistribution |
A Laplace distribution, sometimes called a double exponential distribution.
|
LaplaceDistribution.CDF |
CDF of the Laplace distribution.
|
LaplaceDistribution.MaximumLikelihoodEstimator |
Estimates the ML parameters of a Laplace distribution from a
Collection of Numbers.
|
LaplaceDistribution.PDF |
The PDF of a Laplace Distribution.
|
LaplaceDistribution.WeightedMaximumLikelihoodEstimator |
Creates a UnivariateGaussian from weighted data
|
LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>> |
A linear mixture of RandomVariables, with a prior probability distribution.
|
LogisticDistribution |
A implementation of the scalar logistic distribution, which measures the
log-odds of a binary event.
|
LogisticDistribution.CDF |
CDF of the LogisticDistribution
|
LogisticDistribution.PDF |
PDF of the LogisticDistribution
|
LogNormalDistribution |
Log-Normal distribution PDF and CDF implementations.
|
LogNormalDistribution.CDF |
CDF of the Log-Normal Distribution
|
LogNormalDistribution.MaximumLikelihoodEstimator |
Maximum Likelihood Estimator of a log-normal distribution.
|
LogNormalDistribution.PDF |
PDF of a Log-normal distribution
|
LogNormalDistribution.WeightedMaximumLikelihoodEstimator |
Maximum Likelihood Estimator from weighted data
|
MixtureOfGaussians |
Creates a probability density function (pdf) comprising of a collection of
MultivariateGaussian and corresponding prior probability distribution that
a particular MultivariateGaussian generates observations.
|
MixtureOfGaussians.EMLearner |
An Expectation-Maximization based "soft" assignment learner.
|
MixtureOfGaussians.Learner |
A hard-assignment learner for a MixtureOfGaussians
|
MixtureOfGaussians.PDF |
PDF of the MixtureOfGaussians
|
MultinomialDistribution |
A multinomial distribution is the multivariate/multiclass generalization
of the Binomial distribution.
|
MultinomialDistribution.Domain |
Allows the iteration through the set of subsets.
|
MultinomialDistribution.Domain.MultinomialIterator |
An Iterator over a Domain
|
MultinomialDistribution.PMF |
Probability Mass Function of the Multinomial Distribution.
|
MultivariateGaussian |
The MultivariateGaussian class implements a multidimensional Gaussian
distribution that contains a mean vector and a covariance matrix.
|
MultivariateGaussian.IncrementalEstimator |
The estimator that creates a MultivariateGaussian from a stream of
values.
|
MultivariateGaussian.IncrementalEstimatorCovarianceInverse |
The estimator that creates a MultivariateGaussian from a stream of values
by estimating the mean and covariance inverse (as opposed to the
covariance directly), without ever performing a matrix inversion.
|
MultivariateGaussian.MaximumLikelihoodEstimator |
Computes the Maximum Likelihood Estimate of the MultivariateGaussian
given a set of Vectors
|
MultivariateGaussian.PDF |
PDF of a multivariate Gaussian
|
MultivariateGaussian.SufficientStatistic |
Implements the sufficient statistics of the MultivariateGaussian.
|
MultivariateGaussian.SufficientStatisticCovarianceInverse |
Implements the sufficient statistics of the MultivariateGaussian while
estimating the inverse of the covariance matrix.
|
MultivariateGaussian.WeightedMaximumLikelihoodEstimator |
Computes the Weighted Maximum Likelihood Estimate of the
MultivariateGaussian given a weighted set of Vectors
|
MultivariateGaussianInverseGammaDistribution |
A distribution where the mean is selected by a multivariate Gaussian and
a variance parameter (either for a univariate Gaussian or isotropic Gaussian)
is determined by an Inverse-Gamma distribution.
|
MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>> |
A LinearMixtureModel of multivariate distributions with associated PDFs.
|
MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> |
PDF of the MultivariateMixtureDensityModel
|
MultivariatePolyaDistribution |
A multivariate Polya Distribution, also known as a Dirichlet-Multinomial
model, is a compound distribution where the parameters of a multinomial
are drawn from a Dirichlet distribution with fixed parameters and a constant
number of trials and then the observations are generated by this
multinomial.
|
MultivariatePolyaDistribution.PMF |
PMF of the MultivariatePolyaDistribution
|
MultivariateStudentTDistribution |
Multivariate generalization of the noncentral Student's t-distribution.
|
MultivariateStudentTDistribution.PDF |
PDF of the MultivariateStudentTDistribution
|
NegativeBinomialDistribution |
Negative binomial distribution, also known as the Polya distribution,
gives the number of successes of a series of Bernoulli trials before
recording a given number of failures.
|
NegativeBinomialDistribution.CDF |
CDF of the NegativeBinomialDistribution
|
NegativeBinomialDistribution.MaximumLikelihoodEstimator |
Maximum likelihood estimator of the distribution
|
NegativeBinomialDistribution.PMF |
PMF of the NegativeBinomialDistribution.
|
NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator |
Weighted maximum likelihood estimator of the distribution
|
NormalInverseGammaDistribution |
The normal inverse-gamma distribution is the product of a univariate
Gaussian distribution with an inverse-gamma distribution.
|
NormalInverseGammaDistribution.PDF |
PDF of the NormalInverseGammaDistribution
|
NormalInverseWishartDistribution |
The normal inverse Wishart distribution
|
NormalInverseWishartDistribution.PDF |
PDF of the normal inverse-Wishart distribution.
|
ParetoDistribution |
This class describes the Pareto distribution, sometimes called the Bradford
Distribution.
|
ParetoDistribution.CDF |
CDF of the Pareto Distribution.
|
ParetoDistribution.PDF |
PDF of the ParetoDistribution
|
PoissonDistribution |
A Poisson distribution is the limits of what happens when a Bernoulli trial
with "rare" events are sampled on a continuous basis and then binned into
discrete time intervals.
|
PoissonDistribution.CDF |
CDF of the PoissonDistribution
|
PoissonDistribution.MaximumLikelihoodEstimator |
Creates a PoissonDistribution from data
|
PoissonDistribution.PMF |
PMF of the PoissonDistribution.
|
PoissonDistribution.WeightedMaximumLikelihoodEstimator |
Creates a PoissonDistribution from weighted data.
|
ScalarDataDistribution |
A Data Distribution that uses Doubles as its keys, making it a univariate
distribution
|
ScalarDataDistribution.CDF |
CDF of the ScalarDataDistribution, maintains the keys/domain in
sorted order (TreeMap), so it's slower than it's peers.
|
ScalarDataDistribution.Estimator |
Estimator for a ScalarDataDistribution
|
ScalarDataDistribution.PMF |
PMF of the ScalarDataDistribution
|
ScalarMixtureDensityModel |
ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density
model.
|
ScalarMixtureDensityModel.CDF |
CDFof the SMDM
|
ScalarMixtureDensityModel.EMLearner |
An EM learner that estimates a mixture model from data
|
ScalarMixtureDensityModel.PDF |
PDF of the SMDM
|
SnedecorFDistribution |
CDF of the Snedecor F-distribution (also known as Fisher F-distribution,
Fisher-Snedecor F-distribution, or just plain old F-distribution).
|
SnedecorFDistribution.CDF |
CDF of the F-distribution.
|
StudentizedRangeDistribution |
Implementation of the Studentized Range distribution, which defines the
population correction factor when performing multiple comparisons.
|
StudentizedRangeDistribution.APStat |
This is a translation of Fortran code taken from
http://lib.stat.cmu.edu/apstat/, and the comments on the individual functions
in this class are taken directly from the original.
|
StudentizedRangeDistribution.CDF |
CDF of the StudentizedRangeDistribution
|
StudentizedRangeDistribution.SampleRange |
Computes the estimate of the Studentized Range for a single sample
|
StudentTDistribution |
Defines a noncentral Student-t Distribution.
|
StudentTDistribution.CDF |
Evaluator that computes the Cumulative Distribution Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
StudentTDistribution.MaximumLikelihoodEstimator |
Estimates the parameters of the Student-t distribution from the given
data, where the degrees of freedom are estimated from the Kurtosis
of the sample data.
|
StudentTDistribution.PDF |
Evaluator that computes the Probability Density Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
StudentTDistribution.WeightedMaximumLikelihoodEstimator |
Creates a UnivariateGaussian from weighted data
|
UniformDistribution |
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
UniformDistribution.CDF |
Cumulative Distribution Function of a uniform
|
UniformDistribution.MaximumLikelihoodEstimator |
Maximum Likelihood Estimator of a uniform distribution.
|
UniformDistribution.PDF |
Probability density function of a Uniform Distribution
|
UniformIntegerDistribution |
Contains the (very simple) definition of a continuous Uniform distribution,
parameterized between the minimum and maximum bounds.
|
UniformIntegerDistribution.CDF |
Implements the cumulative distribution function for the discrete
uniform distribution.
|
UniformIntegerDistribution.MaximumLikelihoodEstimator |
Implements a maximum likelihood estimator for the discrete uniform
distribution.
|
UniformIntegerDistribution.PMF |
Probability mass function of a discrete uniform distribution.
|
UnivariateGaussian |
This class contains internal classes that implement useful functions based
on the Gaussian distribution.
|
UnivariateGaussian.CDF |
CDF of the underlying Gaussian.
|
UnivariateGaussian.CDF.Inverse |
Inverts the CumulativeDistribution function.
|
UnivariateGaussian.ErrorFunction |
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.
|
UnivariateGaussian.ErrorFunction.Inverse |
Inverse of the ErrorFunction
|
UnivariateGaussian.IncrementalEstimator |
Implements an incremental estimator for the sufficient statistics for
a UnivariateGaussian.
|
UnivariateGaussian.MaximumLikelihoodEstimator |
Creates a UnivariateGaussian from data
|
UnivariateGaussian.PDF |
PDF of the underlying Gaussian.
|
UnivariateGaussian.SufficientStatistic |
Captures the sufficient statistics of a UnivariateGaussian, which are
the values to estimate the mean and variance.
|
UnivariateGaussian.WeightedMaximumLikelihoodEstimator |
Creates a UnivariateGaussian from weighted data
|
WeibullDistribution |
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.
|
WeibullDistribution.CDF |
CDF of the Weibull distribution
|
WeibullDistribution.PDF |
PDF of the Weibull distribution
|
YuleSimonDistribution |
The Yule-Simon distribution is a model of preferential attachment, such as
a model of the number of groups follows a power-law distribution
(Zipf's Law).
|
YuleSimonDistribution.CDF |
CDF of the Yule-Simon Distribution
|
YuleSimonDistribution.PMF |
PMF of the Yule-Simon Distribution
|