Blurb::
Initial number of samples for sampling-based methods
Description::
The ``initial_samples`` keyword is used to define the number of initial
samples (i.e., randomly chosen sets of variable values) at which to
execute a model.  The initial samples may later be augmented in an
iterative process.

*Default Behavior*

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

*Usage Tips*

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 ``variance_based_decomp``, we recommend hundreds to
thousands of samples.  Note that for ``variance_based_decomp``, the
number of simulations performed will be N*(dim+2).
Topics::

Examples::

.. code-block::

    method
      sampling
        sample_type random
        initial_samples = 20
        refinement_samples = 5


Theory::

Faq::

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