Blurb::
Number of samples for sampling-based methods

Description::
The \c samples keyword is used to define the number of samples (i.e.,
randomly chosen sets of variable values) at which to execute a model.

<b> Default Behavior </b>

By default, Dakota will use the minimum number of samples required by
the chosen method.

<b> Usage Tips </b>

To obtain linear sensitivities or to construct a linear response
surface, at least dim+1 samples should be used, where "dim" is the
number of variables.  For sensitivities to quadratic terms or
quadratic response surfaces, at least (dim+1)(dim+2)/2 samples are
needed.  For uncertainty quantification, we recommend at least 10*dim
samples.  For \c variance_based_decomp, we recommend hundreds to
thousands of samples.  Note that for \c variance_based_decomp, the
number of simulations performed will be N*(dim+2).


Topics::	
Examples::
\verbatim
method
  sampling
    sample_type lhs
    samples = 20
\endverbatim
Theory::
Faq::
See_Also::	
