Package smile.stat.distribution
Interface ExponentialFamily
 All Superinterfaces:
Distribution
,Serializable
 All Known Implementing Classes:
BetaDistribution
,ChiSquareDistribution
,ExponentialDistribution
,GammaDistribution
,GaussianDistribution
The exponential family is a class of probability distributions sharing
a certain form. The normal, exponential, gamma, chisquare, beta, Weibull
(if the shape parameter is known), Dirichlet, Bernoulli, binomial,
multinomial, Poisson, negative binomial, and geometric distributions
are all exponential families. The family of Pareto distributions with
a fixed minimum bound form an exponential family.
The Cauchy, Laplace, and uniform families of distributions are not exponential families. The Weibull distribution is not an exponential family unless the shape parameter is known.
The purpose of this interface is mainly to define the method M that is the Maximization step in the EM algorithm. Note that distributions of exponential family has the closeform solutions in the EM algorithm. With this interface, we may allow the mixture contains distributions of different form as long as it is from exponential family.
 See Also:

Method Summary
Modifier and TypeMethodDescriptionM
(double[] x, double[] posteriori) The M step in the EM algorithm, which depends on the specific distribution.Methods inherited from interface smile.stat.distribution.Distribution
cdf, entropy, inverseTransformSampling, length, likelihood, logLikelihood, logp, mean, p, quantile, quantile, quantile, rand, rand, rejectionSampling, sd, variance

Method Details

M
The M step in the EM algorithm, which depends on the specific distribution. Parameters:
x
 the input data for estimationposteriori
 the posteriori probability. Returns:
 the (unnormalized) weight of this distribution in the mixture.
