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
|