smile.stat.distribution

Interface Distribution

All Known Implementing Classes:

AbstractDistribution, BernoulliDistribution, BetaDistribution, BinomialDistribution, ChiSquareDistribution, DiscreteDistribution, DiscreteExponentialFamilyMixture, DiscreteMixture, EmpiricalDistribution, ExponentialDistribution, ExponentialFamilyMixture, FDistribution, GammaDistribution, GaussianDistribution, GaussianMixture, GeometricDistribution, HyperGeometricDistribution, KernelDensity, LogisticDistribution, LogNormalDistribution, Mixture, NegativeBinomialDistribution, PoissonDistribution, ShiftedGeometricDistribution, TDistribution, WeibullDistribution

public interface Distribution

Probability distribution of univariate random variable. A probability distribution identifies either the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous). When the random variable takes values in the set of real numbers, the probability distribution is completely described by the cumulative distribution function, whose value at each real x is the probability that the random variable is smaller than or equal to x.

Author:

Haifeng Li

See Also:

MultivariateDistribution

Method Summary

Methods

Modifier and Type	Method and Description
double	cdf(double x) Cumulative distribution function.
double	entropy() Shannon entropy of the distribution.
double	likelihood(double[] x) The likelihood of the sample set following this distribution.
double	logLikelihood(double[] x) The log likelihood of the sample set following this distribution.
double	logp(double x) The density at x in log scale, which may prevents the underflow problem.
double	mean() The mean of distribution.
int	npara() The number of parameters of the distribution.
double	p(double x) The probability density function for continuous distribution or probability mass function for discrete distribution at x.
double	quantile(double p) The quantile, the probability to the left of quantile is p.
double	rand() Generates a random number following this distribution.
double	sd() The standard deviation of distribution.
double	var() The variance of distribution.

Method Detail

npara

int npara()

The number of parameters of the distribution.

mean

double mean()

The mean of distribution.

var

double var()

The variance of distribution.

sd

double sd()

The standard deviation of distribution.

entropy

double entropy()

Shannon entropy of the distribution.

rand

double rand()

Generates a random number following this distribution.

p

double p(double x)

The probability density function for continuous distribution or probability mass function for discrete distribution at x.

logp

double logp(double x)

The density at x in log scale, which may prevents the underflow problem.

cdf

double cdf(double x)

Cumulative distribution function. That is the probability to the left of x.

quantile

double quantile(double p)

The quantile, the probability to the left of quantile is p. It is actually the inverse of cdf.

likelihood

double likelihood(double[] x)

The likelihood of the sample set following this distribution.

logLikelihood

double logLikelihood(double[] x)

The log likelihood of the sample set following this distribution.