Generative models contrast with discriminative models, in that a generative model is a full probabilistic model of all variables, whereas a discriminative model provides a model only for the target variable(s) conditional on the observed variables.
A typical subset of the optimization problems is estimating parameters of a generative model given a set of data generated form the model.
In the last few years, there has been a real movement of the discipline in three different directions: Neural networks, statistics, generative models, Bayesian inference There is a sense in which these fields are coalescing.
similarity measure between instances, and it is reasonable to assume that experts working in the specific application domain have already identified valid similarity measures, particularly in areas such as information retrieval and generative models.
in 623 dimensions (for 32-bit values), and runs faster than all but the least statistically desirable generators. It is now increasingly becoming the "random number generator of choice" for statistical simulations and generative modeling.
 See also: Machine learning, Distribution, Regression, Pattern recognition, Neural network
  
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