pilot_samples

Initial set of samples for groups in the multilevel BLUE sampling method

Specification

• Alias: initial_samples

• Arguments: INTEGERLIST

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Optional		independent	Independent pilot sampling for groups in multilevel BLUE

Description

The pilot sample provides initial estimates of group covariances during the first iteration of the multilevel best linear unbiased estimator (ML BLUE) sampling approach. These initial estimates then guide the algorithm toward group sample increments in order to achieve a prescribed error at minimum total cost or minimum error for a prescribed budget.

The formulation of ML BLUE assumes independent samples per group, and the pilot sampling can either start in this same vein (see independent) or start from group covariances inferred from a single set of shared samples. The benefit in the latter shared pilot approach is one can avoid investing any pilot samples for groups that not determined to be active in the resource allocation process. Groups that are determined to be active, however, must start from new sets of independent samples, not reusing the shared samples.

Default Behavior

The default number of pilot samples is 100. The default pilot sampling approach is shared, where a single set of pilot samples is evaluated across all models. The group covariances are initially formed from this shared set of pilot samples for purposes of solving the initial resource allocation problem. Group covariances are then updated using new independent sample sets for all groups with new allocations.

Usage Tips

The number of specified values can be none (default values used for all groups), one (all groups use the same specified value), or the number of sample groups. The number of groups is determined by combinations of the model indices, including the truth model and all approximations. Larger counts in the number of combinations may be throttled as described in group_throttle.