# initial_samples

Initial number of samples for sampling-based methods

**Specification**

*Alias:*None*Arguments:*INTEGER*Default:*model-dependent

**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).

**Examples**

```
method
sampling
sample_type random
initial_samples = 20
refinement_samples = 5
```