.. _method-approximate_control_variate-search_model_graphs:

"""""""""""""""""""
search_model_graphs
"""""""""""""""""""


Perform a recursion of admissible DAGs for a given model ensemble



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

   method-approximate_control_variate-search_model_graphs-model_selection
   method-approximate_control_variate-search_model_graphs-no_recursion
   method-approximate_control_variate-search_model_graphs-kl_recursion
   method-approximate_control_variate-search_model_graphs-partial_recursion
   method-approximate_control_variate-search_model_graphs-full_recursion


**Specification**

- *Alias:* None

- *Arguments:* None

- *Default:* NO_GRAPH_RECURSION


**Child Keywords:**

+-------------------------+--------------------+-----------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword        | Dakota Keyword Description                    |
|                         | Group              |                       |                                               |
+=========================+====================+=======================+===============================================+
| Optional                                     | `model_selection`__   | Perform a recursion of admissible model       |
|                                              |                       | subsets for a given model ensemble            |
+-------------------------+--------------------+-----------------------+-----------------------------------------------+
| Required (Choose One)   | DAG Ensemble       | `no_recursion`__      | Do not recur over admissible DAGs for a given |
|                         | Generation Option  |                       | model ensemble                                |
|                         |                    +-----------------------+-----------------------------------------------+
|                         |                    | `kl_recursion`__      | Model graph enumeration that follows the      |
|                         |                    |                       | ACV-KL partitioning scheme                    |
|                         |                    +-----------------------+-----------------------------------------------+
|                         |                    | `partial_recursion`__ | Perform a partial recursion of admissible     |
|                         |                    |                       | DAGs                                          |
|                         |                    +-----------------------+-----------------------------------------------+
|                         |                    | `full_recursion`__    | Perform a full recursion of all admissible    |
|                         |                    |                       | DAGs for a given model ensemble               |
+-------------------------+--------------------+-----------------------+-----------------------------------------------+

.. __: method-approximate_control_variate-search_model_graphs-model_selection.html
__ method-approximate_control_variate-search_model_graphs-no_recursion.html
__ method-approximate_control_variate-search_model_graphs-kl_recursion.html
__ method-approximate_control_variate-search_model_graphs-partial_recursion.html
__ method-approximate_control_variate-search_model_graphs-full_recursion.html



**Description**


Within the context of generalized ACV (:cite:p:`Bomarito2022`),
search over a set of admissible directed acyclic graphs (DAGs) for a
given model ensemble.  The DAG with the best performance (lowest
estimator variance for a prescribed budget or lowest cost for a
prescribed accuracy) is selected for final post-processing.

In order of increasing numbers of DAGs to enumate, options include
``kl_recursion``, ``partial_recursion`` with a ``depth_limit``, and
``full_recursion``.




**Theory**


Refer to :cite:p:`Bomarito2022` for additional details on generalized ACV recursion.



