.. _method-multilevel_sampling-weighted-search_model_graphs:

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


For weighted multilevel Monte Carlo, model graph selection options become available



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

   method-multilevel_sampling-weighted-search_model_graphs-model_selection


**Specification**

- *Alias:* None

- *Arguments:* None

- *Default:* NO_GRAPH_RECURSION


**Child Keywords:**

+-------------------------+--------------------+---------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword      | Dakota Keyword Description                    |
|                         | Group              |                     |                                               |
+=========================+====================+=====================+===============================================+
| Optional                                     | `model_selection`__ | Select the best subset of approximations      |
|                                              |                     | within weighted multilevel Monte Carlo        |
+----------------------------------------------+---------------------+-----------------------------------------------+

.. __: method-multilevel_sampling-weighted-search_model_graphs-model_selection.html



**Description**


Referring to :dakkw:`method-approximate_control_variate-acv_recursive_diff`,
weighted MLMC is a special case of ACV-RD, resulting in a forward to
the generalized ACV solver for the case of a fixed hierarchical DAG.

While the DAG is fixed, other search options in generalized
ACV become available -- see
:dakkw:`method-multilevel_sampling-weighted-search_model_graphs-model_selection`.