pick_and_freeze

Use the pick-and-freeze variance-based decomposition method

Specification

• Alias: None

• Arguments: None

Description

Uses structured samples to compute main and total effect sensitivity indices.

Default Behavior

If the user specified a number of samples, \(N\), and a number of nondeterministic variables, \(M\), pick-and-freeze variance-based decomposition requires the evaluation of \(N*(M+2)\) samples.

Warning

Specifying this keyword will increase the number of function evaluations above the number requested with the samples keyword since replicated sets of sample values are evaluated.

Expected Output

When pick_and_freeze is specified as the vbd_sampling_method, sensitivity indices for main effects and total effects will be reported. Main effects (roughly) represent the percent contribution of each individual variable to the variance in the model response. Total effects represent the percent contribution of each individual variable in combination with all other variables to the variance in the model response.

Examples

method,
  sampling
    sample_type lhs
    samples = 100
    variance_based_decomp
      vbd_sampling_method pick_and_freeze

Theory

Pick-and-freeze methods are currently the most popular approach for varianced-based sensitivity index computation, but they incur significant computational cost. These approaches rely on structured sampling wherein two independent random sample sets of the input variables are generated, then the random samples of the variable whose sensitivity index is being computed are substituted from one sample set into the other. Specifically, if the user specified a number of samples, \(N\), and a number of nondeterministic variables, \(M\), pick-and-freeze variance-based decomposition requires the evaluation of \(N*(M+2)\) samples.