Uses of Class
smile.stat.distribution.AbstractDistribution

Packages that use AbstractDistribution

Package	Description
smile.stat.distribution	Probability distributions.

Uses of AbstractDistribution in smile.stat.distribution

Subclasses of AbstractDistribution in smile.stat.distribution

Modifier and Type	Class and Description
class	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.
class	BetaDistribution The beta distribution is defined on the interval [0, 1] parameterized by two positive shape parameters, typically denoted by α and β.
class	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.
class	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.
class	DiscreteDistribution This is the base class of univariate discrete distributions.
class	DiscreteExponentialFamilyMixture The finite mixture of distributions from discrete exponential family.
class	DiscreteMixture The finite mixture of discrete distributions.
class	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.
class	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.
class	ExponentialFamilyMixture The finite mixture of distributions from exponential family.
class	FDistribution F-distribution arises in the testing of whether two observed samples have the same variance.
class	GammaDistribution The Gamma distribution is a continuous probability distributions with a scale parameter θ and a shape parameter k.
class	GaussianDistribution The normal distribution or Gaussian distribution is a continuous probability distribution that describes data that clusters around a mean.
class	GaussianMixture Finite univariate Gaussian mixture.
class	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, …}.
class	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.
class	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.
class	LogNormalDistribution A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
class	Mixture A finite mixture model is a probabilistic model for density estimation using a mixture distribution.
class	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.
class	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.
class	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, …}.
class	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.
class	WeibullDistribution The Weibull distribution is one of the most widely used lifetime distributions in reliability engineering.