binned

Use the binned Sobol’ main effect index computation

Specification

• Alias: None

• Arguments: None

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Optional		num_bins	Number of bins used to compute the variance-based decomposition

Description

Uses unstructured input-output samples to estimate main effect indices. It cannot compute total indices.

Expected Output Sensitivity indices for main effects only will be reported. Main effects (roughly) represent the percent contribution of each individual variable to the variance in the model response.

Examples

method,
  sampling
    sample_type lhs
    samples = 100
    variance_based_decomp
      vbd_sampling_method binned

Theory

The binned approach to computing Sobol’ main effect indices is introduced in [LM16]. As opposed to pick-and-freeze approaches like [STCR04], it does not require a specific sampling structure. Given a set of randomly-generated input-output samples, it computes the main effect index by binning samples, computing a sample statistic for each bin, then computing another sample statistic over the bins.

Two algorithms are detailed in [LM16]: computing a sample expectation for each bin, then a sample variance, or computing a sample variance for each bin, then an expectation. The second algorithm is implemented in Dakota.