Since parametric models in audio usually require very many parameters, the features are used to summarize properties of multiple parameters in a more compact or salient representation.
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Some are parametric models (Linear Regression, Discriminant Analysis).
Others are non parametric (Histograms, Decision Trees).
Numerosity reduction, where the data are replaced or estimated by alternative, smaller data representations such as parametric models (which need store only the model parameters instead of the actual data) or nonparametric methods such as clustering, ...
Thus, in a sense this is the most general fully parametric model (as opposed to Cox's proportional hazard model which is non-parametric), and estimates can be obtained for a variety of different underlying survival distributions.
Parametric models typically have greater efficiency at the cost of more specific assumptions about the data, but, it is important to verify that the distributional assumption is indeed valid.
 See also: Distribution, Regression, Variance, Estimation, Neural network
  
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