.. _method-global_interval_est:

"""""""""""""""""""
global_interval_est
"""""""""""""""""""


Interval analysis using global optimization methods



**Topics**


uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation


.. toctree::
   :hidden:
   :maxdepth: 1

   method-global_interval_est-samples
   method-global_interval_est-seed
   method-global_interval_est-max_iterations
   method-global_interval_est-convergence_tolerance
   method-global_interval_est-max_function_evaluations
   method-global_interval_est-sbgo
   method-global_interval_est-ego
   method-global_interval_est-ea
   method-global_interval_est-lhs
   method-global_interval_est-rng
   method-global_interval_est-model_pointer


**Specification**

- *Alias:* nond_global_interval_est 

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+------------------------------+---------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword               | Dakota Keyword Description                  |
|                         | Group              |                              |                                             |
+=========================+====================+==============================+=============================================+
| 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  | `sbgo`__                     | 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                                      |
+----------------------------------------------+------------------------------+---------------------------------------------+

.. __: method-global_interval_est-samples.html
__ method-global_interval_est-seed.html
__ method-global_interval_est-max_iterations.html
__ method-global_interval_est-convergence_tolerance.html
__ method-global_interval_est-max_function_evaluations.html
__ method-global_interval_est-sbgo.html
__ method-global_interval_est-ego.html
__ method-global_interval_est-ea.html
__ method-global_interval_est-lhs.html
__ method-global_interval_est-rng.html
__ method-global_interval_est-model_pointer.html



**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 :ref:`topic-variable_support` for information on supported
variable types.