sampling

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• Keywords Area
• method
• sampling

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Randomly samples variables according to their distributions

Topics

This keyword is related to the topics:

• uncertainty_quantification
• sampling

Specification

Alias: nond_sampling

Argument(s): none

	Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
	Optional		sample_type	Selection of sampling strategy
	Optional		variance_based_decomp	Activates global sensitivity analysis based on decomposition of response variance into contributions from variables
	Optional		backfill	Ensures that the samples of discrete variables with finite support are unique
	Optional		fixed_seed	Reuses the same seed value for multiple random sampling sets
	Optional		reliability_levels	Specify reliability levels at which the response values will be estimated
	Optional		response_levels	Values at which to estimate desired statistics for each response
	Optional		distribution	Selection of cumulative or complementary cumulative functions
	Optional		probability_levels	Specify probability levels at which to estimate the corresponding response value
	Optional		gen_reliability_levels	Specify generalized relability levels at which to estimate the corresponding response value
	Optional		rng	Selection of a random number generator
	Optional		samples	Number of samples for sampling-based methods
	Optional		seed	Seed of the random number generator
	Optional		model_pointer	Identifier for model block to be used by a method

Description

This method generates parameter values by drawing samples from the specified uncertain variable probability distributions. The computational model is executed over all generated parameter values to compute the responses for which statistics are computed. The statistics support sensitivity analysis and uncertainty quantification.

Default Behavior

By default, sampling methods operate on aleatory and epistemic uncertain variables. The types of variables can be restricted or expanded (to include design or state variables) through use of the active keyword in the variables block in the Dakota input file. If continuous design and/or state variables are designated as active, the sampling algorithm will treat them as parameters with uniform probability distributions between their upper and lower bounds. Refer to variable_support for additional information on supported variable types, with and without correlation.

The following keywords change how the samples are selected:

• sample_type
• fixed_seed
• rng
• samples
• seed
• variance_based_decomp

Expected Outputs

As a default, Dakota provides correlation analyses when running LHS. Correlation tables are printed with the simple, partial, and rank correlations between inputs and outputs. These can be useful to get a quick sense of how correlated the inputs are to each other, and how correlated various outputs are to inputs. variance_based_decomp is used to request more sensitivity information, with additional cost.

Additional statistics can be computed from the samples using the following keywords:

• response_levels
• reliability_levels
• probability_levels
• gen_reliability_levels

response_levels computes statistics at the specified response value. The other three allow the specification of the statistic value, and will estimate the corresponding response value.

distribution is used to specify whether the statistic values are from cumulative or complementary cumulative functions.

Usage Tips

sampling is a robust approach to doing sensitivity analysis and uncertainty quantification that can be applied to any problem. It requires more simulations than newer, advanced methods. Thus, an alternative may be preferable if the simulation is computationally expensive.

Examples

# tested on Dakota 6.0 on 140501

environment
  tabular_data
    tabular_data_file = 'Sampling_basic.dat'

method
  sampling
    sample_type lhs
    samples = 20

model
  single

variables
  active uncertain
  uniform_uncertain = 2
    descriptors  =   'input1'     'input2'
    lower_bounds =  -2.0     -2.0
    upper_bounds =   2.0      2.0
  continuous_state = 1
    descriptors =   'constant1'
    initial_state = 100

interface
  analysis_drivers 'text_book'
    fork

responses
  response_functions = 1
  no_gradients
  no_hessians

This example illustrates a basic sampling Dakota input file.

• LHS is used instead of purely random sampling.
• The default random number generator is used.
• Without a seed specified, this will not be reproducable
• In the variables block, two types of variables are used
• Only the uncertain variables are varied, this is the default behavior, and is also specified by the active keyword, w/ the uncertain option

See Also

These keywords may also be of interest:

• active
• incremental_lhs

FAQ

Q: Do I need to keep the LHS* and S4 files? A: No