Finite mixture models provide a flexible framework for analyzing a variety of data.
They are parametric models that enable you to describe an unknown distribution in terms of mixtures of known distributions.
They enable you to assess the probabilities of events or simulate draws from the unknown
distribution the same way you do when your data are from a known distribution.
The SAS/STAT finite mixture models procedures include the following:
FMM Procedure
The FMM procedure fits statistical models to data for which the distribution of the response
is a finite mixture of univariate distributions–that is, each response comes from one of
several random univariate distributions with unknown probabilities.
The following are highlights of the FMM procedure's features:
 model the component distributions in addition to the mixing probabilities
 fit finite mixture models by maximum likelihood or Bayesian methods
 fit finite mixtures of regression and generalized linear models
 define the model effects for the mixing probabilities and their link function
 model overdispersed data
 estimate multimodal or heavytailed densities
 fit zeroinflated or hurdle models to count data with excess zeros
 fit regression models with complex error distributions
 classify observations based on predicted component probabilities
 twenty five different response distributions
 linear equality and inequality constraints on model parameters

 specify the response variable by using either the response syntax or the events/trials syntax
 automated model selection for homogeneous mixtures
 weighted estimation
 control the performance characteristics of the procedure (for example, the number of CPUs, the number of threads for multithreading, and so on)
 obtain separate analyses on observations in groups
 create a data set that contains observationwise statistics that are computed after fitting the model
 create a SAS data set corresponding to any output table
 automatically create graphs by using ODS Graphics

For further details, see
FMM Procedure