| 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 |
Chi-square (or chi-squared) 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 |
F-distribution 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 non-parametric 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 log-normal 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 (non-random) 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 t-distribution (or simply the t-distribution) 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 non-cumulative distribution.