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/******************************************************************************
 *                   Confidential Proprietary                                 *
 *         (c) Copyright Haifeng Li 2011, All Rights Reserved                 *
 ******************************************************************************/

package smile.stat.distribution;

/**
 * The exponential family is a class of probability distributions sharing
 * a certain form. The normal, exponential, gamma, chi-square, 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 distributuions of exponential * family has the close-form 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 ExponentialFamilyMixture * @see DiscreteExponentialFamily * @see DiscreteExponentialFamilyMixture * * @author Haifeng Li */ public interface ExponentialFamily { /** * The M step in the EM algorithm, which depends the specific distribution. * * @param x the input data for estimation * @param posteriori the posteriori probability. * @return the (unnormalized) weight of this distribution in the mixture. */ public Mixture.Component M(double[] x , double[] posteriori); }





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