.. _method-multilevel_sampling-weighted-search_model_graphs-model_selection:

"""""""""""""""
model_selection
"""""""""""""""


Select the best subset of approximations within weighted multilevel Monte Carlo



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



**Specification**

- *Alias:* None

- *Arguments:* None


**Description**


Referring to :dakkw:`method-approximate_control_variate-acv_recursive_diff`,
weighted MLMC is a special case of ACV-RD where a hierarchical DAG is
employed across the model approximations.  As such, a weighted MLMC
specification forwards to the generalized ACV solver, but with fixing
the DAG to be hierarchical (each approximation node points to the next
approximation of higher fidelity, ending with the truth model at the
root node).

While the DAG is fixed, generalized
ACV capabilities for model selection are available for a specification
of ``search_model_graphs model_selection``.  Refer to
:dakkw:`method-approximate_control_variate-search_model_graphs-model_selection`
for additional information on this capability.




**Theory**



Refer to :cite:p:`Bomarito2022` for understanding ACV generalizations
for model selection for a given DAG.