Interface  Description 

DiscreteExponentialFamily 
The purpose of this interface is mainly to define the method M that is
the Maximization step in the EM algorithm.

Distribution 
Probability distribution of univariate random variable.

ExponentialFamily 
The exponential family is a class of probability distributions sharing
a certain form.

MultivariateDistribution 
Probability distribution of multivariate random variable.

MultivariateExponentialFamily 
The purpose of this interface is mainly to define the method M that is
the Maximization step in the EM algorithm.

Class  Description 

AbstractDistribution 
The base class of univariate distributions.

BernoulliDistribution 
Bernoulli distribution is a discrete probability distribution, which takes
value 1 with success probability p and value 0 with failure probability
q = 1  p.

BetaDistribution 
The beta distribution is defined on the interval [0, 1] parameterized by
two positive shape parameters, typically denoted by α and β.

BinomialDistribution 
The binomial distribution is the discrete probability distribution of
the number of successes in a sequence of n independent yes/no experiments,
each of which yields success with probability p.

ChiSquareDistribution 
Chisquare (or chisquared) distribution with k degrees of freedom is the
distribution of a sum of the squares of k independent standard normal
random variables.

DiscreteDistribution 
Univariate discrete distributions.

DiscreteExponentialFamilyMixture 
The finite mixture of distributions from discrete exponential family.

DiscreteMixture 
The finite mixture of discrete distributions.

DiscreteMixture.Component 
A component in the mixture distribution is defined by a distribution
and its weight in the mixture.

EmpiricalDistribution 
An empirical distribution function or empirical cdf, is a cumulative
probability distribution function that concentrates probability 1/n at
each of the n numbers in a sample.

ExponentialDistribution 
An exponential distribution describes the times between events in a Poisson
process, in which events occur continuously and independently at a constant
average rate.

ExponentialFamilyMixture 
The finite mixture of distributions from exponential family.

FDistribution 
Fdistribution arises in the testing of whether two observed samples have
the same variance.

GammaDistribution 
The Gamma distribution is a continuous probability distributions with
a scale parameter θ and a shape parameter k.

GaussianDistribution 
The normal distribution or Gaussian distribution is a continuous probability
distribution that describes data that clusters around a mean.

GaussianMixture 
Finite univariate Gaussian mixture.

GeometricDistribution 
The geometric distribution is a discrete probability distribution of the
number X of Bernoulli trials needed to get one success, supported on the set
{1, 2, 3, …} . 
HyperGeometricDistribution 
The hypergeometric distribution is a discrete probability distribution that
describes the number of successes in a sequence of n draws from a finite
population without replacement, just as the binomial distribution describes
the number of successes for draws with replacement.

KernelDensity 
Kernel density estimation is a nonparametric way of estimating the
probability density function of a random variable.

LogisticDistribution 
The logistic distribution is a continuous probability distribution whose
cumulative distribution function is the logistic function, which appears
in logistic regression and feedforward neural networks.

LogNormalDistribution 
A lognormal distribution is a probability distribution of a random variable
whose logarithm is normally distributed.

Mixture 
A finite mixture model is a probabilistic model for density estimation
using a mixture distribution.

Mixture.Component 
A component in the mixture distribution is defined by a distribution
and its weight in the mixture.

MultivariateExponentialFamilyMixture 
The finite mixture of distributions from multivariate exponential family.

MultivariateGaussianDistribution 
Multivariate Gaussian distribution.

MultivariateGaussianMixture 
Finite multivariate Gaussian mixture.

MultivariateMixture 
The finite mixture of multivariate distributions.

MultivariateMixture.Component 
A component in the mixture distribution is defined by a distribution
and its weight in the mixture.

NegativeBinomialDistribution 
Negative binomial distribution arises as the probability distribution of
the number of successes in a series of independent and identically distributed
Bernoulli trials needed to get a specified (nonrandom) number r of failures.

PoissonDistribution 
Poisson distribution expresses the probability of a number of events
occurring in a fixed period of time if these events occur with a known
average rate and independently of the time since the last event.

ShiftedGeometricDistribution 
The "shifted" geometric distribution is a discrete probability distribution
of the number of failures before the first success, supported on the set
{0, 1, 2, 3, …} . 
TDistribution 
Student's tdistribution (or simply the tdistribution) is a probability
distribution that arises in the problem of estimating the mean of a
normally distributed population when the sample size is small.

WeibullDistribution 
The Weibull distribution is one of the most widely used lifetime distributions
in reliability engineering.

In the discrete case, one can easily assign a probability to each possible value. In contrast, when a random variable takes values from a continuum, probabilities are nonzero only if they refer to finite intervals.
If total order is defined for the random variable, the cumulative distribution function gives the probability that the random variable is not larger than a given value; it is the integral of the noncumulative distribution.