Dakota Reference Manual: global_interval_est

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global_interval_est

Interval analysis using global optimization methods

## Topics

This keyword is related to the topics:

- uncertainty_quantification
- epistemic_uncertainty_quantification_methods
- interval_estimation

## Specification

Alias: nond_global_interval_est

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | samples | Number of samples for sampling-based methods
| Optional | seed | Seed of the random number generator
| Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods
| Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence
| Optional | max_function_evaluations | Number of function evaluations allowed for optimizers
| Optional (Choose One) | Solution Approach (Group 1) | sbo | Use the surrogate based optimization method
ego | Use the Efficient Global Optimization method
ea | Use an evolutionary algorithm
lhs | Uses Latin Hypercube Sampling (LHS) to sample variables
| Optional | rng | Selection of a random number generator
| Optional | model_pointer | Identifier for model block to be used by a method

## Description

In the global approach to interval estimation, one uses either a global optimization method or a sampling method to assess the bounds of the responses.

`global_interval_est` allows the user to specify several approaches to calculate interval bounds on the output responses.

- `lhs` - note: this takes the minimum and maximum of the samples as the bounds
- `ego`
- `sbo`
- `ea`

Additional Resources

Refer to variable_support for information on supported variable types.

## See Also

These keywords may also be of interest:

- global_evidence
- local_evidence
- local_interval_est